Predicting climate heating impacts on riverine fish species diversity in a biodiversity hotspot region
Bibliographic record
Abstract
Co-occurring biodiversity and global heating crises are systemic threats to life on Earth as we know it, especially in relatively rare freshwater ecosystems, such as in Iran. Future changes in the spatial distribution and richness of 131 riverine fish species were investigated at 1481 sites in Iran under optimistic and pessimistic climate heating scenarios for the 2050s and 2080s. We used maximum entropy modeling to predict species' potential distributions by hydrologic unit (HU) occupancy under current and future climate conditions through the use of nine environmental predictor variables. The most important variable determining fish occupancy was HU location, followed by elevation, climate variables, and slope. Thirty-seven species were predicted to decrease their potential habitat occupancy in all future scenarios. The southern Caspian HU faces the highest future species reductions followed by the western Zagros and northwestern Iran. These results can be used by managers to plan conservational strategies to ease the dispersal of species, especially those that are at the greatest risk of extinction or invasion and that are in rivers fragmented by dams.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".