Coastwide evaluation and classification of Pacific Region estuaries based on anthropogenic activities and significant fish habitat
Bibliographic record
Abstract
Estuaries are highly productive and diverse ecosystems that represent a geographic bottleneck between marine and freshwater systems. Estuaries have been identified as ecologically and biologically significant areas (EBSAs) in Canada’s Pacific Region because of their importance for the aggregation, productivity, and fitness of anadromous fishes, including Pacific salmon. However, estuaries are also the site of many anthropogenic activities, and the degradation of estuarine habitats such as eelgrass beds has had corresponding impacts on many species of ecological, economic, and cultural importance. To support a regional request for information to aid integrated coastal planning, a coastwide classification of estuaries based on anthropogenic activities was completed. Anthropogenic activities and associated stressors relevant to estuary habitats were identified through a literature review and used to guide the compilation of spatial datasets. The spatial datasets were then used in a cluster analysis that identified estuaries that share similar activity types and levels of use. Ecological information was then compiled and mapped to highlight how estuarine fishes and fish habitats considered significant or sensitive relate to the results of the clustering analysis and individual estuaries. This broad-scale analysis represents an initial assessment of British Columbia’s estuaries that can help guide localized efforts and identify opportunities for management efficiencies among estuaries that face similar activities and stressors. Research needs for future evaluations at a finer-scale scale are detailed, as are linkages with projects underway within specific estuaries, to highlight opportunities for collaboration as priority estuaries are identified for management and conservation action.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".