Méthodologie pour l'analyse des données forestières historiques : le cas de la forêt expérimentale du Lac Édouard, Québec
Bibliographic record
Abstract
Permanent sampling plots in experimental forests have generated large quantities of data over the past decades. Methods used to collect certain of these data have often varied over the course of the sampling. The objective of this study was to develop a process to evaluate sampling differences in historical forest data. The proposed approach is to: (1) examine available data and seek out the missing ones in archives of the concerned organizations; (2) regroup data by inventory periods; (3) search for the sampling methodology in each of the different inventory periods; and (4) evaluate the impact of differences in methodology on data continuity. When using this approach on the historical data of the Lake Edward experimental forest, we were able to better define the strengths and the weaknesses of the database. Key words: forest inventories, La Mauricie National Park, permanent sample plots, regeneration inventories
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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 | 0.040 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".