Aerial Survey as a Tool to Estimate Abundance and Describe Distribution of a Carcharhinid Species, the Lemon Shark,<i>Negaprion brevirostris</i>
Bibliographic record
Abstract
Aerial survey provides an important tool to assess the abundance of both terrestrial and marine vertebrates. To date, limited work has tested the effectiveness of this technique to estimate the abundance of smaller shark species. In Bimini, Bahamas, the lemon shark (Negaprion brevirostris) shows high site fidelity to a shallow sandy lagoon, providing an ideal test species to determine the effectiveness of localised aerial survey techniques for a Carcharhinid species in shallow subtropical waters. Between September 2007 and September 2008, visual surveys were conducted from light aircraft following defined transects ranging in length between 8.8 and 4.4 km. Count results were corrected for “availability”, “perception”, and “survey intensity” to provide unbiased abundance estimates. The abundance of lemon sharks was greatest in the central area of the lagoon during high tide, with a change in abundance distribution to the east and western regions of the lagoon with low tide. Mean abundance of sharks was estimated at 49 (±8.6) individuals, and monthly abundance was significantly positively correlated with mean water temperature. The successful implementation of the aerial survey technique highlighted the potential of further employment for shark abundance assessments in shallow coastal marine environments.
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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 | 0.002 | 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) | 0.002 | 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".