Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that generate from a first pair and a second pair of images of livestock that are within an enclosure and that are taken at different times using a stereoscopic camera, at least two distance distributions of the aquatic livestock within the enclosure. The distance distributions can be used to determine a measure associated with an optical property of the water within the enclosure. A signal associated with the measure can be provided.

Abstract: An inverse design system combines a large language model (LLM) with a task-specific optimizer, which includes a search function, a forward model, and a comparator. The LLM adjusts parameters of the optimizer's components in response to a design scenario. Then the optimizer processes the design scenario to produce design candidates. Optionally, the LLM learns from the design candidates in an iterative process. A stochastic predictive modeling system combines an LLM with input distributions and a forward model. The LLM adjusts one or more of the input distributions and/or the forward model in response to a forecast scenario. Then the forward model processes a sampling of the input distributions to produce a forward distribution. Optionally, the LLM informs the sampling process. Optionally, the LLM learns from the forward distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for monocular underwater camera biomass estimation. In some implementations, an exemplary method includes obtaining an image of a fish captured by a monocular underwater camera; providing the image of the fish to a depth perception model; obtaining output of the depth perception model indicating a depth-enhanced image of the fish data in the image; determining a biomass value estimate of the fish based on the output; and determining an action based on one or more biomass values estimates including the biomass value estimate of the fish.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction aggregation. In some implementations, an exemplary method includes obtaining images of fish captured by an underwater camera; providing data of the images to a trained model; obtaining output of the trained model indicating the likelihoods that the biomass of fish are within multiple ranges; combining likelihoods of the output based on one or more ranges common to likelihoods of two or more fish to generate a biomass distribution; and determining an action based on the biomass distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing aquatic life data, e.g., aquatic life images. One of the methods includes obtaining aquatic life data; identifying a first subset of the aquatic life data that is to be annotated by one or more data annotators to generate annotated aquatic life data and a second subset of the aquatic life data that is not to be annotated; providing the first subset of the aquatic life data to the one or more data annotators, the one or more data annotators processing the first subset of the aquatic life data to generate the annotated aquatic life data; providing the second subset of the aquatic life data to a data storage curator; and determining whether and, if so, which storage device in one or more storage devices to store the second subset of the aquatic life data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing aquatic life data, e.g., aquatic life image. One of the methods includes receiving aquatic life data comprising a plurality of aquatic life images from a user through a user interface; receiving, within the user interface, a first user request to use the aquatic life data to train a machine learning model; determining a data curator score for each aquatic life image; identifying, based on the data curator scores, a proper subset of the plurality of aquatic life images; providing the proper subset of the plurality of aquatic life images to one or more data annotators; receiving annotation data generated by the one or more data annotators; and providing the annotation data to a training system configured to train the machine learning model by using the annotation data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction aggregation. In some implementations, an exemplary method includes obtaining images of fish captured by an underwater camera; providing data of the images to a trained model; obtaining output of the trained model indicating the likelihoods that the biomass of fish are within multiple ranges; combining likelihoods of the output based on one or more ranges common to likelihoods of two or more fish to generate a biomass distribution; and determining an action based on the biomass distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that generate from a first pair and a second pair of images of livestock that are within an enclosure and that are taken at different times using a stereoscopic camera, at least two distance distributions of the aquatic livestock within the enclosure. The distance distributions can be used to determine a measure associated with an optical property of the water within the enclosure. A signal associated with the measure can be provided.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for combining a simulator and neural network for predictions, such as predictions of Harmful Algal Bloom (HAB). One of the methods includes obtaining an estimate of a first number of marine-life cells representative of growth of the marine-life within a first region; providing the estimate to a prediction system that comprises (i) an ocean simulator and (ii) a trained network model, wherein the trained network model is trained to provide an indication of marine-life change using output from the ocean simulator; obtaining output from the prediction system, wherein the output indicates a second number of cells representative of growth of the marine-life within a second region; comparing the output to an output threshold; and in response to the output satisfying the output threshold, performing an action.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for Measuring and Quantifying Biodiversity in an Environment. One of the methods includes receiving a set of images representing the marine environment; identifying, by one or more processing devices within the set of images, objects representing marine life in the marine environment; classifying, by the one or more processing devices, the objects into multiple clusters based on feature vectors identified for each of the objects; and computing, by the one or more processing devices based on attributes associated with the multiple clusters, a metric indicative of the biodiversity in the marine environment.

Abstract: In an aspect, there is provided a method that includes: obtaining a physics-based ocean simulation model that specifies multiple simulation parameters, obtaining data characterizing an ocean in a geographical region, determining initial values of the simulation parameters based on data characterizing the ocean in the geographical region, and determining adjusted values of the simulation parameters. Determining adjusted values can include performing a simulation using the initial values of the simulation parameters to determine an initial simulation output, evaluating a task-specific objective function that is based on a measure of disagreement between: (i) the initial simulation output, and (ii) a target value for the ocean in the geographical region, and determining adjusted values of the simulation parameters of the physics-based ocean simulation model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting features of an aquatic ecosystem. One of the methods includes generating, using ground truth data, first training input, wherein the first training input includes training labels; generating an augmented dataset from multiple data sources as second training input, wherein the augmented dataset is generated using (i) bathymetric data and (ii) simulated data based on satellite data indicating one or more coastal ecosystems; and training the machine learning model using (i) the first training input and (ii) second training input, such that the machine learning model is trained to predict biomass growth.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction aggregation. In some implementations, an exemplary method includes obtaining images of fish captured by an underwater camera; providing data of the images to a trained model; obtaining output of the trained model indicating the likelihoods that the biomass of fish are within multiple ranges; combining likelihoods of the output based on one or more ranges common to likelihoods of two or more fish to generate a biomass distribution; and determining an action based on the biomass distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction. In some implementations, an exemplary method includes obtaining one or more images of a population of fish captured by an underwater camera; providing data corresponding to the one or more images to a model trained to predict biomass values; obtaining output of the trained model including a predicted biomass value indicating a future biomass of a fish within the population of fish; and determining an action based on the predicted biomass value.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for monocular underwater camera biomass estimation. In some implementations, an exemplary method includes obtaining a plurality of images of fish captured by a monocular underwater camera; providing the plurality of images that were captured by the monocular underwater camera to a first model trained to detect one or more fish within the plurality of images; generating one or more values for each detected fish as a set of values; generating a biomass distribution of the fish based on the set of values; and determining an action based on the biomass distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for distribution-based machine learning. In some implementations, a method for distribution-based machine learning includes obtaining fish images from a camera device; generating predicted values using a machine learning model and one or more of the fish images; comparing the predicted values to distribution data representing features of multiple fish; and updating one or more parameters of the machine learning model based on the comparison.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for monocular underwater camera biomass estimation. In some implementations, an exemplary method includes obtaining an image of a fish captured by a monocular underwater camera; providing the image of the fish to a depth perception model; obtaining output of the depth perception model indicating a depth-enhanced image of the fish data in the image; determining a biomass value estimate of the fish based on the output; and determining an action based on one or more biomass values estimates including the biomass value estimate of the fish.