Capturerecapture estimates of space used in streams (CRESUS) at the population scale: case study on<i>Zingel asper</i>(percid), a threatened species of the Rhône catchment
Bibliographic record
Abstract
In this study, capturemarkrecapture statistics were applied to spatial recapture histories to assess the intensity of fish restricted movements along the longitudinal axis of a river using a previously described model for survival and recruitment analysis. Adapting the stopover estimation method to spatial data, movement probabilities were then used to estimate space used at the population scale. This capturerecapture estimates of space used in streams (CRESUS) method may thus be seen as a complementary tool of classic home range methods and should be used to explore the consequence of behavioural strategies on population mechanisms. We propose a methodological example where movements and space use strategies of a Zingel asper (percid) population in the Beaume River (Ardèche, France) were directly estimated at the population scale taking account of the effects of different biotic or abiotic factors. Results showed differences in Z. asper space use patterns among sexes, periods of biological cycle (growing and spawning period), and types of mesohabitat. Downstream movements were more important during the spawning period and by the way the riffle was more intensively used.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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".