Bibliographic record
Abstract
The Limit Reference Point (LRP) represents the upper bound of stock states that should be avoided in order to prevent serious harm to the stock and is the boundary between the Critical and Cautious zones of DFO’s Precautionary Approach (PA) Policy. Under that Policy and the Fish Stocks Provisions (FSPs), breaching the LRP is a trigger for a rebuilding plan. Serious harm is an undesirable state that may be irreversible or only slowly reversible over the long-term. It may be directly or indirectly due to fishing, other human-induced impacts, or natural causes, and occurs at states before extirpation is a concern. Loss of stock structure (e.g., depletion of subunits) is not typically included in descriptions of serious harm but can meet the definition of serious harm if it constitutes a loss of stock productivity or resilience. A stock can be defined based on the management unit, assessment unit, and/or biological unit. Scale mismatch occurs when there is misalignment in time or space between these units, management or assessment activities, or biological processes. Consequences of scale mismatch can include over- or under-estimation of stock biomass and exploitation rates, as well as impacts to reference points, stock status metrics, and the risk of serial depletion of subunits. Best-practice principles are provided to give overarching guidance and recommendations for selecting, estimating and updating indicators, LRPs and stock status metrics under the PA Policy and FSPs. These encompass scenarios ranging from data-rich to data-limited, where more than one indicator or model may be used for advice, where scale mismatch may be occurring or where the perception of stock status has changed between assessments. Future revisions to Canadian harvest strategy policy should include default guidance for LRPs based on estimates of unfished biomass. Gaps were identified for future work, predominately related to non-stationarity in conditions affecting productivity when defining LRPs, as well as spatial reference points, the impact of climate forcing on scale mismatch, and for situations when it may be desirable to set an LRP that accommodates ecosystem functions.
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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 | 0.024 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.016 |
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".