Advancements in Riverine Fish Movement Modeling: Bridging Environmental Complexity and Fish Behavior
Bibliographic record
Abstract
Understanding fish movement and response in relation to their environment near infrastructure and migratory barriers is crucial for developing sustainable fisheries management solutions. Intermediate-scale (time scales of minutes to days and spatial scales less than 2 km) movement models are a contemporary approach for understanding and predicting movement patterns of riverine fish in light of their changing environment, which is predominately water flow (i.e., flow direction, flow magnitude, and rates of change). These models can be complex and require interdisciplinary knowledge. For more than 60 years, different approaches have been developed for investigating, reproducing, and predicting the movement outcomes of fish decision making. Due to the breadth of model frameworks available, a systematic review is helpful to summarize the available knowledge including a description of general model properties, environment modeling, agent characteristics, and methods of data use, output, and validation. The analysis of 38 studies found a wide range of model frameworks and architectures. Despite the lack of consistency, each model imposed some combination of the following behaviors: response to flow direction (i.e., rheotaxis), response to flow velocity magnitude, response to turbulence, response to depth, and memory/experience of the individual. There is a clear need for more consistent modeling approaches, increased consideration of memory/experience, inclusion of a wider range of species, incorporation of more detailed environmental covariates, and use of time-dependent solutions in fish movement models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".