Deep learning habitat modeling for moving organisms in rapidly changing estuarine environments: A case of two fishes
Bibliographic record
Abstract
Modeling the spatial distribution of mobile organisms under rapidly changing environmental conditions is a challenging endeavor that has to be undertaken whenever the impacts of alterations have to be assessed in dynamic scenarios. We modeled habitat suitability for Lake sturgeon ( Acipenser fulvescens ) and White perch ( Morone americana , both had have been followed by hydro-acoustic telemetry) in an estuarine river section with rapidly changing tidal and hydrodynamic conditions using deep feed-forward Artificial Neural Networks (ANN). Descriptors used were of many types: intrinsic features (species, sexual maturity and gender, and individual character), terrain features, hydraulic and tidal conditions, and time. A set of ANN models with varying degree of complexity, in terms of their number of hidden layers, number of nodes per layers, and regularization parameters, were tried and evaluated using cross-validation. The best model has three layers with 100, 50, and 20 nodes and classified 94.0 % of observations as presence (and 60.6 % of pseudo absences as absences, overall correct classification: 77.3 % ) during the trials. The study highlights that tidal and hydraulic models, coupled with acoustic telemetry and machine learning, can be used to predict the spatial distribution of mobile organisms even in extremely variable ecosystems such as estuaries .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".