Baynes Sound and Lambert Channel; An ecosystem in need
Bibliographic record
Abstract
Canada adopted the Canada’s Oceans Act in 1996 of which was to provide the framework for an integrated ecosystems approach to the management of Canadian oceans inclusive of coastal zones and including areas considered ecologically or biologically significant (EBSA). EBSAs were developed as a management tool and are intended to identify areas in need of enhanced management that supersedes the management needs of individual species. Core criteria for an EBSA include, unique and rare, distinct features; aggregation, including areas where most individuals of a species are aggregated for some part the year; fitness consequences defined as areas that are used by species for life history activity(ies) and that make a significant contribution to the fitness of individuals of those species. An EBSA would meet one or more of these three core criteria. Baynes Sound/Lambert Channel meet all three criteria. These combined features make Baynes Sound/Lambert Channel one of the most unique and biologically sensitive regions along coastal BC. Baynes Sound is also a region experiencing a number of competing economic stressors, notably an as yet regulated shellfish industry and seaweed harvest. In BC, there is increased recognition of the impacts of the shellfish industry and seaweed harvest on ecosystem structure and function. The recent mandate letter to Mr. Tootoo, Minister of Fisheries, Oceans and the Canadian Coast Guard mandated that the Minster “work with the Minister of Environment and Climate Change to increase the proportion of Canada’s marine and coastal areas that are protected – to five percent by 2017, and ten percent by 2020”. Baynes Sound/Lambert Channel is an area that needs to be protected. In this presentation the rationale for recognizing the importance of this region as an ESBA and the need for economic benefits from this region to be developed within an ESBA framework are presented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".