Number, seasonal movements, and residency characteristics of river dolphins in an Amazonian floodplain lake system
Bibliographic record
Abstract
The size and structure of a community of Amazon river dolphins or botos, Inia geoffrensis (de Blainville, 1817), was investigated using boat surveys and long-term observations of recognisable animals. Year-round, some 260 botos occurred in or near the 225-km2 Mamirauá várzea floodplain lake system, of which half were permanent residents by our definition. Seasonal variation in water levels influenced distribution between habitats but not the overall number of botos. Ninety percent of marked botos encountered within the lake system were permanent residents. There appeared to be a cline in site fidelity between those that always live in or near the system and those that visit at intervals of years. We estimated that 270 botos were "significant users" of the lake system (i.e., occurred within it for sufficient periods in a year to be observed at least once) and that many others visited for short periods. Individuals moved many tens to hundreds of kilometres along the rivers, but there was no broad-scale seasonal migration. The boto population of the central Amazon, at least, may be structured on the basis of floodplain lake systems, with extensive animal movement between systems. We estimate that 13 000 botos occur in the 11 240 km2 Mamirauá Sustainable Development Reserve, which covers an estimated 11%–18% of várzea habitat in Brazil.
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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 | 0.001 |
| 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) | 0.001 | 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".