Density, abundance and group size of river dolphins (Inia geoffrensis and Sotalia fluviatilis) in Central Amazonia, Brazil
Bibliographic record
Abstract
Given the difficulties and costs often associated with surveying cetaceans, enlisting members of the public to collect data offers a promisingalternative approach. Comparison of cetacean ‘participatory science’ (also known as ‘citizen science’) data with data collected during traditionalscientific studies helps reveal the strengths and weaknesses of a participatory science approach. With a large number of vessel operators on thewater throughout the year, including dolphin-oriented tour boats, the Hawaiian Islands offer an ideal study site to employ such a dual-methodcomparison. The study aimed to enhance understanding of nearshore dolphin distributions relative to bathymetry. Operators of tour and fishingvessels within the shallow Maui Nui basin of the Hawaiian Islands were recruited to report delphinid sightings. Researchers conducted standarddolphin surveys within the same region. The participatory science approach was successful in generating a large sample size of sightings from fivedifferent species. Findings here demonstrate the potential value of participatory science and of using a multimethod approach to infer odontocetedistribution trends relative to bathymetry in areas where both methods are feasible. Important refinements for future projects are highlighted.
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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".