Modelling population viability in fragmented environments: contribution to the conservation of an endangered percid (<i>Zingel asper</i>)
Bibliographic record
Abstract
Zingel asper is an endemic percid of the Rhône catchment. The demography and habitat use of this endangered benthic species have been studied in previous works. The species exhibits natural low densities and clumped distribution related to habitat distribution. Based on these results, the authors build a stochastic equations network model, integrating spatial structure at intrapopulation level and vague density dependence. Calculation of density, growth rates, and local extinction rates globally match the field results. The numerical sensitivity analysis on extinction probabilities shows that adult and juvenile survival rates and spawning success (due to random events) are the key parameters of intrapatch dynamics. Low negative variations in these parameters increase extinction probabilities. The number of available connected patches and the dispersal rate drive the population persistence at the interpatch scale. Population extinction probability over 100 years is at least 0.4 for dispersal rate below 0.2, or when the number of connected patches is below 15. These results enlighten the role of dispersal in nonmigratory fish populations and should be useful in assessing the impact of riverine habitat fragmentation through river-damming and habitat loss.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".