Bibliographic record
Abstract
Biogeoclimatic Ecosystem Classifi cation (BEC) and Biogeoclimatic (BGC) mapping form the cornerstone to sound ecologically-based resource management in British Columbia. It is used a large number of public, industrial, private and NGO resource practitioners to manage the landbase. The most comprehensive BEC classifi cation for the former Nelson Forest Region (NFR) was completed in 1992. This was followed by publication of a fi eld guide characterizing the site series for 18 subzones and variants of the NFR. A preliminary assessment indicates there are major defi ciencies in the BEC classifi cation which have signifi cant consequences for those using the system as a basis for planning and implementing resource management activities. These defi ciencies include failure to recognize and describe all non-forested ecosystems and many very dry and very wet forested ecosystems. In summary, 57 % of the subzones lack a site series classifi cation and 33 % of the described site series are based on an inadequate number of replicate samples. The long-term goal of this project is to revise the BEC classifi cation and produce a new fi eld guide for the NFR. For the 2003/04 fi scal year, our short-term objectives are to locate and acquire additional BEC sample data for the NFR, complete a comprehensive sampling gap analysis for potential BEC types in the NFR, classifi cation system, integrate plot data for BGC subzones shared by the Kamloops and Nelson Forest Regions and produce a revised BEC classifi cation and fi eld guide materials for some of these shared
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.517 | 0.292 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".