Vegetation classification standard for Canada Workshop: 3 1 May - 2 June 2000
Bibliographic record
Abstract
The workshop, "Vegetation Classification Standard for Canada" was held in Hull (now Gatineau), Quebec, 31 May-2 June 2000.Representatives from a wide spectrum of Canadian federal agencies, territorial and provincial agencies, and conservation organizations throughout Canada, as well as representatives from the U.S. and Mexico, participated.The 20 presentations covered: the importance of a national vegetation classification; the International Classification of Ecological Communities (ICEC); history of the Canadian project; review of classification work in Canada; the status of vegetation classifications in the Canadian territories and provinces, along with some case studies; the development of a Canadian Forest Ecosystem Classification (FEC); and, a proposal for a Canadian National Vegetation Classification (CNVC).Two key decisions made at the workshop were that (1) the ICEC should be the basis from which to develop a CNVC as a standard, and (2) a CNVC Working Group should be established and consist of two sub-groups: a Steering Committee and a Technical Committee.We anticipate that decisions made at and after this workshop will have a substantial impact on how ecological communities are classified and used for conservation throughout Canada and beyond.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.047 |
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".