Central Coast Cumulative Effects Project
Bibliographic record
Abstract
The Central Coast Cumulative Effects Project is an invited collaboration of the Kitasoo Xai’xais, Nuxalk, and Wuikinuxv Nations in the region of the Central Coast of what is now known as British Columbia, Canada. Nations in the region face decisions on the management of land and marine use in their territories, against a backdrop of pressures of climate change, growing habitat fragmentation, depleted fish populations, and proposed large energy and mining projects. There is consensus that in order to understand ecological consequences of future land and marine use, decision-makers need broader understanding of the cumulative effects on key species present in the region to date. Our goal is to provide a regional cumulative effects assessment that is driven by local values and knowledge, to support decision-making in the management of adverse consequences of cumulative effects, now and into the next 25 years. In this project, we ask how cumulative effects of past and present pressures impact the health of species across land and sea, where ‘health’ is the ability of a species to be self-sustaining and fulfill its ecological and cultural roles. Our team focused on valued species (or groups of species)from the coast that each collaborating Nation prioritized for management–Pacific salmon, old growth forest, grizzly and black bears, Dungeness crabs, Pacific herring, marbled murrelets, and rhinoceros auklets. Our objective is to produce visual, spatial, and written summaries of pressures acting on each species in the region, as well as predictions of how each species will respond to cumulative effects under various development scenarios into the future. Our work can inform ongoing land and marine use decisions by the Nations, as well as provide a baseline assessment of cumulative effects on certain species in the region should a larger development and assessment process be proposed.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.184 | 0.037 |
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".