An ecogeographic framework for in situ conservation of forest trees in British Columbia
Bibliographic record
Abstract
We present a comprehensive approach to carry out community-wide assessments of in situ conservation of forest trees based on basic botanical and ecological data. This is a first step, resulting in a consistent framework to set priorities for collection and inclusion of species- specific biological and genetic information. We use botanical sample data to generate high-resolution distribution maps as a basis for a gap analysis of how well each species is represented in protected areas. To account for adaptive genetic variation of tree species we stratify populations by ecological zones that represent different macroclimates. In a detailed example for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), we show that under certain conditions this approach can pinpoint gaps at the level of genetically differentiated populations without actually using genetic data. In a comprehensive case study, evaluating the outcome of a major protected area expansion between 1991 and 2001 for British Columbia, we demonstrate how extensive results from a community-wide GIS analysis can be summarized and presented for decision-making. We provide methods to identify and efficiently cope with in situ conservation gaps, where lack of data or low protected area coverage requires additional conservation efforts or collection of better data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".