Group Size, Habitat Use and Behavioral Ecology of Amazonian River Dolphins (Inia geoffrensis and Sotalia fluviatilis) in the Pacaya-Samiria National Reserve, Peru
Bibliographic record
Abstract
Presentation Title: River dolphins globally represent a highly at risk group of mammals. Most river dolphin species inhabit the world’s large rivers, which are also highly populated and heavily utilized. The focus of my research was on the two species of freshwater dolphins that inhabit the Amazon River, the boto (Inia geoffrensis) and the tucuxi (Sotalia fluviatilis). Currently both species are listed by the International Union for Conservation of Nature (IUCN) as ‘Data Defficient’, which means there is currently not enough information known about them to accurately assess whether or not they are endangered. Major gaps in research of these two species currently exist in many basic biological and ecological parameters. The focus of this research was to compare existing data on group size and habitat preference as well as determine the behavioral ecology of the two species of river dolphins. The research was conducted in the Pacaya-Samiria National Reserve, Peru, which has a relatively high density of river dolphins. The reserve consists of many small tributaries, which are relatively understudied and so the conclusions drawn from this research will help guide future research and management decisions in other regions of the Amazon.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".