Subjective judgement in data subsetting: implications for CPUE standardisation and stock assessment of non-target chondrichthyans
Bibliographic record
Abstract
Standardisation of catch-per-effort (CPUE) data is an essential component for nearly all stock assessments. The first step in CPUE standardisation is to separate the comparable from the non-comparable catch and effort records and this is normally done based on subjective rules. In the present study, we used catch-and-effort data from the elephant fish (Callorhinchus milii) to illustrate the differences in CPUE when using expert judgement to define different ad hoc selection criteria used to subset these data. The data subsets were then used in the standardisation of CPUE and the stock assessment of elephant fish. The catch-and-effort subsets produced different patterns of precision and trends, each of which led to different estimates (and related uncertainty) of model parameters and management reference points. For most CPUE series, there was a very high probability that the elephant fish stock is overexploited and that overfishing is occurring. The estimates of total allowable catch (TAC) and the uncertainty around these estimates also varied considerably depending on the CPUE series used. Our study shows how sensitive TAC estimation is when there is high uncertainty in the definition of the fishing effort targeted at the species analysed.
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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 | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".