Direct and indirect estimates of the productive capacity of fish habitat under Canada’s Policy for the Management of Fish Habitat: where have we been, where are we now, and where are we going?
Bibliographic record
Abstract
No net loss of productive capacity (PC) of fish habitat has been the central concept guiding Canadian fish habitat management policy since 1986. The purpose of this paper is to describe the concept of PC, to review the history and application of the fish habitat management policy in Canada, and to provide a critical review of the range of potential approaches to estimating PC. The approaches were grouped by their central focus: habitat, individual, population, and community–ecosystem. A set of case studies is used to illustrate the use of some approaches drawn from freshwater and marine contexts. Ten components to assessing no net loss of PC were developed and used in the review of approaches for evaluating potential limitations. The review also highlighted the likely future direction of method development, with increasing emphasis on dynamic models integrating population responses to habitat supply characteristics. More work needs to be done to turn research-based metrics of PC into practical operational management assessment tools and to better quantify the link between habitat structure and function and fisheries productivity. The evolving approaches to measure PC reinforce the ties that fish habitat management has to the emerging practices in ecosystem-based management.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".