Bias in spatiotemporal index standardization models caused by unbalanced sampling designs and allocation schemes
Bibliographic record
Abstract
Abstract Relative indices of abundance are key to providing science advice for the management of many fish stocks. Historical focus has been on design-based estimation, wherein mathematical formulae for aggregating data into a single index are directly informed by the underlying sampling design. Recent efforts have moved toward using index standardization models instead. However, the impacts sampling designs and allocation schemes have on these index standardization models have not been fully examined. Using a spatio-temporal multinomial index standardization model developed for survey data obtained with longline fishing gear, we develop a simulation framework to analyze the effects of five different combinations of sampling designs and allocation schemes under various conditions. These simulations expose bias in model-based indices caused by non-proportional-to-area designs, which are common in surveys that have historically utilized design-based estimation. We explore the impact of robust statistical alternatives on this bias. This effort highlights an important source of bias and the importance of periodically reassessing allocation schemes and sampling designs for scientific surveys.
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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 | 0.216 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".