Population, density estimates, and conservation of river dolphins (<i>Inia</i>and<i>Sotalia</i>) in the Amazon and Orinoco river basins
Bibliographic record
Abstract
Abstract This study is part of an on‐going effort to evaluate and monitor river dolphin populations in South America. It comprises the largest initiative to estimate population size and densities ofIniaandSotaliadolphins using statistically robust and standardized methods. From May 2006 to August 2007, seven visual surveys were conducted in selected large rivers of Bolivia, Colombia, Brazil, Ecuador, Peru, and Venezuela in the Amazon and Orinoco river basins. Population sizes ofIniaandSotaliawere estimated for different habitats (main river, tributary, lake, island, confluence, and channel). A total of 291 line and 890 strip transects were conducted, covering a distance of 2,704 linear kilometers. We observed 778Inia geoffrensis, 1,323Inia boliviensis, and 764Sotalia fluviatilis. High‐density areas were identified (within 200 m from the river banks, confluences, and lakes) and we propose that these constitute critical habitat for river dolphins. High densities of river dolphins seem to coincide with well‐managed freshwater protected areas and should be considered as hot spots for river dolphins in South America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".