An empirically based simulation model to inform flow management for endangered species conservation
Bibliographic record
Abstract
Increasing water demand, water development, and ongoing climate change have driven extensive changes to the hydrology, geomorphology and biology of arid-land rivers globally, driving an increasing need to understand how annual hydrologic conditions affect the distribution and abundance of imperiled desert fish populations. We analyzed the relationship between annual hydrologic conditions and the endangered Rio Grande silvery minnow (Hybognathus amarus) in the Middle Rio Grande, New Mexico, USA, using hurdle models to predict both presence and density as a function of integrated annual hydrologic metrics. Both presence and density were positively related to spring high flow magnitude and duration and negatively related to summer drying, as indicated by an integrated flow metric. Simulations suggest hydrologic conditions near the wettest observed in the data set would be required to meet recovery goals in a single year in all reaches. We demonstrate how the models developed herein can be used to examine alternative water management strategies, including strategies that may currently be socially and logistically infeasible to implement, to identify strategies minimizing trade-offs between conservation and other management goals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".