Assessing abundance of populations with limited data: Lessons learned from data-poor fisheries stock assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Estimation of population abundances in the absence of good observational data are notoriously difficult, yet urgently needed for biodiversity conservation and sustainable use of natural resources. In the field of fisheries research, management regulations have long demanded population abundance estimates even if data available are sparse, leading to the development of a range of fish stock assessment methods designed for data-poor populations. Here, we present methods developed within the context of fisheries research that can be applied to conduct population abundance estimations when facing data-limitations. We begin the review from the less data-demanding approaches and continue with more data-intensive ones. We discuss the advantages and caveats of these approaches, the challenges and management implications associated with data-poor stock assessments, and we propose the implementation of the Bayesian hierarchical framework as the most promising avenue for future development and improvement of the current practices.
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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 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it