Ecological conclusions remain unchanged for white sharks in South Africa: A reply to Gennari et al. 2024
Bibliographic record
Abstract
A recent Letter to the Editor by Gennari et al. (2024) contends that methodological issues and data uncertainties may be obscuring declines in abundance of the white shark population in South Africa in the analyses of Bowlby et al. (2023). We have addressed their critiques using scientifically accepted analytical understanding to demonstrate why our ecological conclusions remain unchanged: (1) the relative abundance of white sharks has not exhibited systematic increases or declines at a regional level since protection in 1991, and (2) observed data on human-shark incidents are consistent with the hypothesis that white sharks have partially redistributed along the South African coastline. Future long-term, standardized monitoring throughout South Africa would be expected to substantially reduce uncertainty about the population trend and status of white sharks. Ultimately, the lack of abundance increase following protection remains concerning and necessitates continued conservation efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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; both teacher heads agree on what is shown here.
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".