A generalization of the three-stage model for advice using the precautionary approach in fisheries, to apply broadly to ecosystem properties and pressures
Bibliographic record
Abstract
Abstract Rice, J. C. 2009. A generalization of the three-stage model for advice using the precautionary approach in fisheries, to apply broadly to ecosystem properties and pressures. – ICES Journal of Marine Science, 66: 433–444. The six assumptions of the three-stage model for fisheries advice using a precautionary approach are itemized. The general applicability of each is considered for use with any indicator of ecosystem status, or human pressure on the ecosystem indicator, rather than just spawning-stock biomass (SSB) and fishing mortality. The framework is fully generalizable, at least conceptually, without requiring additional assumptions or extensions that are less plausible than the assumptions already made in fisheries applications. However, application of the three-stage framework in fisheries hinges on the existence of some relationship between stock productivity and SSB as the basis for selecting a limit reference point and positioning a precautionary reference point. Three types of relationship can exist in fisheries data, and the framework has strategies for dealing with each. In an ecosystem application, the notion of a relationship between productivity and the amount of an ecosystem feature is sometimes appropriate, but sometimes the response variable may be resilience of the feature to perturbation or its ability to serve some ecosystem function as a function of the extent of the feature. The generalized framework accommodates all three types of response in five functional relationships, each of which can allow locating a candidate limit reference point.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".