Points à considérer pour l'autorisation de relevés scientifiques menés à l'aide d'engins entrant en contact avec le fond dans les aires protégées de la région de Terre-Neuve-et-Labrador
Bibliographic record
Abstract
Canada is working toward protecting 25% of the country’s oceans by 2025, and 30% by 2030 through the creation of a network of protected areas. These areas have been created to guard sensitive benthic taxa and critical fish habitat from anthropogenic activities such as the potential damaging effects of commercial fishing with bottom trawls and other bottom-contacting gears. Fisheries and Oceans Canada (DFO) and its research partners conduct research surveys using (often similar) bottom contacting gears. The footprint of these surveys is magnitudes lower than that of commercial bottom trawl fishing; nevertheless, managers must evaluate the impacts vs. benefits of scientific surveys in relation to these closures in order to determine if the operation of these surveys within the protected areas pose an unacceptable risk relative to the conservation objectives of those areas. Here we summarize research on the potential impacts of bottom-contact fishing in relation to sensitive benthic taxa. Analyses for the various protected areas suggest that the impacts of ongoing research activities that use bottom-contacting gears within the protected areas are minimal and should not hinder the conservation objectives of those closures. While bottom-contacting surveys are valuable for monitoring benthic taxa within protected areas, other less-destructive methodologies are available that could likely collect equal or better-quality data on these species. However, the loss of these survey data within the protected areas would create time-varying bias in general ecosystem indicators and some of the species-specific survey indices used to assess marine resources of commercial and biological interest in the broader ecosystem. The exclusion of oceanographic data collected within protected areas results in small decreases in estimated temperatures that differ among the closures and exclusion scenarios investigated. Mitigation measures that could be applied to lessen the impacts of surveys on protected areas are discussed, though some would be difficult (at best) to apply without compromising existing survey standardized time series and could take several years to evaluate the feasibility of their implementation. This information is presented in support of a DFO Canadian Science Advisory process that took place on October 5–9, 2020. This report and the advisory process do not provide decisions on authorizing survey activities in the protected areas within the Newfoundland and Labrador (NL) Region, rather they provide the background information necessary to support those decisions.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".