Evaluating ecological representation within differing planning objectives for the central coast of British Columbia
Bibliographic record
Abstract
Maintaining representation of a full range of ecosystem types is a widely accepted strategy to conserve biodiversity in protected areas. We evaluated representation in the central coast region of British Columbia, a forested landbase containing a complex mix of management options, administrative and ownership types, and disparate ecological and economic objectives. We found that most ecosystem types were well represented outside areas subject to management activities, but a minority were poorly represented. When we examined areas under consideration for protection or special management, we found that they failed to represent many of the most poorly represented ecosystem types and incorporated limited amounts of the remainder. Because these poorly represented types were relatively limited in area, it should be possible to adjust proposed reserve areas to improve representation of these types with limited impact on other values. Failing to do so will result in increased opportunity costs to improve representation in the future. Despite the limitations of ecological classification systems to represent biodiversity, they are an improvement over strictly ad hoc approaches because they employ a systematic, repeatable approach in selecting reserve areas.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".