Reflections on the biogeoclimatic approach to ecosystem classification of forested landscape.
Bibliographic record
Abstract
The biogeoclimatic approach to ecosystem classification is unique in that it defines, albeit arbitrarily, an ecosystem, and draws from several of the European and North American schools of vegetation and environment classifications. Undisputedly, the classification has provided a predictive tool for foresters in British Columbia and has given impetus for developing similar classifications elsewhere. The aim of this classification system is to organize forest ecosystems according to relationships in climate, vegetation, site quality, and time. The system is vegetation driven and features three independent, but connected classifications: zonal, vegetation, and site. Site classification is a primary tool used for identifying quality of forest sites. Furthermore, it provides a framework for accumulated, site-specific knowledge about ecological characteristics of plant species, sites, and ecosystems. As a result, the site classification supports a variety of stand - and forest-level decisions as well as forest productivity research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".