Bibliographic record
Abstract
An ecological forest inventory was conducted from 1986 to 2000 in the portion of the Quebec province south of the 52nd parallel. The resulting database contains data from 28,425 ecological relevs located throughout the different bioclimatic domains of Quebec. For each relev, information was gathered on both the vegetation composition and physical environment. Vegetation composition is described by several variables including tree species composition, floristic composition and species cover of different height strata for woody vascular plants as well as cover of herbaceous vascular plants, bryophytes and lichens. Variables describing the physical environment include altitude, slope inclination and aspect, microrelief, bedrock geology and surficial deposit, drainage, humus type, stoniness and soil texture. Data from these relevs form the basic structure underlying the hierarchal ecological classification system of the Ministre des Ressources naturelles et de la Faune (MRNF). The data was also used to develop ecological classification field guides specific to each of the province's ecological regions, providing detailed ecological knowledge to forest managers throughout the province. Finally, data from ecological relevs were used to develop Canadian National Vegetation Classifications (CNVC) vegetation associations. This report describes the available content in the Vegetation Database of Qubec (MRNF) (GIVD ID NA-CA-002).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".