Synthesizing knowledge of the Claybelt to promote sustainable forest management
Bibliographic record
Abstract
A review of the scientific literature relevant to the Claybelt region was undertaken under the initiative of Lake Abitibi Model Forest (LAMF) and in collaboration with the Canadian Forest Service, the Ontario and Quebec Ministries of Natural Resources and the NSERC-UQAT-UQAM (Natural Sciences and Engineering Research Council Université du Québec en Abitibi-Témiscamingue Université du Québec à Montréal) Industrial Chair in Sustainable forest Management. The objective was to synthesize this information in order to develop better forestry practices and identify knowledge and research gaps. Forestry-related knowledge was gathered on six broad topics: i) natural disturbances, ii) forest ecosystems, iii) past and present forest practices, iv) biological diversity, v) forest management and vi) examples of current applications of natural disturbance-based forest management. The work allowed us to synthesize a large body of knowledge into one publication that will be a useful reference for foresters in both provinces. Key words: biodiversity, Claybelt, even-aged/uneven-aged forests, fire, silvicultural practices
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".