Développement dun outil de classification de la structure des peuplements et comparaison de deux territoires de la pessière à mousses du Québec
Bibliographic record
Abstract
Forest stand structure is an important element for biodiversity and, from a sustainable forest management perspective, uneven-sized stands should be managed in order to maintain the structural diversity over the landscape. The first objective of this study is to develop a statistical tool to characterize stand structure that can be used in forest management planning. The second objective is to classify the stand structure of two regions to illustrate a possible use for the tool. The statistical tool for characterizing stand structure has been developed from forest inventory data gathered by the ministère des Ressources naturelles du Québec, using discriminant analysis. The analysis makes it possible to classify the stands into three types of structure, even-sized, uneven-sized and inverse J-shaped, with an error rate estimated at only 7%. Proportions of different structure types in Quebecs eastern black spruce forest region have been compared with those found in the western black spruce forest region. Nearly 90% of the western black spruce forest region is composed of pure black spruce stands, contrary to the eastern black spruce region, where there are more pure fir and mixed spruce-fir stands. Most of the western black spruce forest stands are even-sized (62%), while almost 70% of the eastern black spruce forest stands are uneven-sized or inverse J-shaped. Pure black spruce stands are more even-sized than pure fir stands, but regional differences are also found within pure black spruce stands. Our results show that it is possible to develop a robust tool that makes it possible to classify thousands of stands rapidly. Such tools are required if we want to consider stand structure for appropriate management prescriptions in the boreal forest. Key words: Even- and uneven-sized structure, fire regime, Picea mariana, Abies balsamea, boreal forest, structural diversity
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".