On ‘variability’ as a sampling artefact: the case of <i>Sardinella</i> in north‐western Africa
Bibliographic record
Abstract
Objective evaluation of the global impact of fisheries on ocean ecosystems may be hampered by various biases suggesting natural variability of exploited species to be stronger and more widespread than is really the case. One of these is reporting biases: papers are usually not published which show that nothing has changed. Another such bias is that much variability is fishery‐induced, i.e. due to the truncation by fishing of the age composition of exploited populations. A third source of bias, emphasized here, is that resulting from sampling a migrating population with a fixed device. This bias is illustrated by contrasting the relatively stable echo‐acoustic estimates of biomass of Sardinella spp. along the north‐west African coast, i.e. from Morocco and Mauritania to Senegal (data from 1992–98), with the more variable estimates of biomass in the waters of each of these countries. We conclude that published reports of ‘variability’ in exploited species should explicitly account for the effect of migrations and other movements, especially when such reports are to be used for contrasting fisheries‐induced with environmental impacts on biomass.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.010 | 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 teacher head, 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".