Physical habitat simulation for small-sized characid fish species from tropical rivers in Brazil
Bibliographic record
Abstract
ABSTRACT Physical habitat simulation (PHABSIM) is an important step of the instream flow incremental methodology (IFIM), which is applied to establish environmental flow regimes. This study applied the PHABSIM in two reaches of the Velhas river basin, whose long-term discharges are similar but are under different degrees of impact. Suitability curves were obtained for fish species using traditional methods (Astyanax sp., Piabarchus stramineus, Piabina argentea and Serrapinnus heterodon) and generalized additive models for fish density (Astyanax sp., P. argentea and S. heterodon). The results of habitat use depended on the method for curves generation. Applying the suitability curves by traditional methods, different discharge scenarios were simulated. The flow increasing from a dry scenario to a discharge of 1 year of return promotes a possible habitat increase for all species. However, the same hydrological flow percentiles produce different habitat proportions in different rivers. This work demonstrates that regardless of how suitability curves for the Neotropical species are generated, caution should be taken when applying them. However, the PHABSIM method allows more complex analyses than the traditional approaches based on minimal flow estimations, which is usually applied in South America.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".