Population size estimates of pink river dolphins (Inia geoffrensis) using mark-recapture methods on photo-identification
Bibliographic record
Abstract
Population size estimates of pink river dolphins (Inia geoffrensis) are critical to assess the conservation status of this species and the impacts of increasing human stressors in freshwater ecosystems. Photo-identifications of Inia dolphins were collected between February 2007 and August 2009 in two locations of the Colombian Amazon and Orinoco river basins. Population sizes of Inia were obtained by using the closed (Petersen) mark-recapture model on photo-identifications. The total population size estimate for right/left side individuals was 129/71 Inia dolphins (CV=0.36/0.35) in the Amazon location and 125/58 Inia dolphins (CV=0.77/0.69) in the Orinoco location. The survey and analysis protocols were designed to try to meet mark-recapture assumptions. However, photo-identification was incomplete in both study areas, and there could be recruitment and unequal probabilities of capture due to preferences of individuals for certain areas. Further effort should be focused towards expanding the photo-identification catalogues and creating long-term monitoring programs.
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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 | 0.001 |
| 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".