Les milieux humides au Québec : quelle protection face aux enjeux du développement économique, notamment résidentiel ?
Bibliographic record
Abstract
Wetlands, endowed with a natural heritage and outstanding features, represent a major component of the Quebec landscape. In spite of this, these ecosystems are threatened by social and economic development which often takes precedence over their conservation. In this context, governmental authorities have developed a certain number of tools to protect them. Some municipalities require, inter alia, that any construction taking place on grounds suspected of accommodating a wetland be analyzed by an expert beforehand. This work focuses primarily on identifying, delineating, and characterizing wetlands. It endeavours to establish an outline of the naturel environment's components in order to identify the environmental legal constraints surrounding the site development. Through the achievement of such a study, this report seeks to address the issue of wetland protection in Quebec and to highlight the benefits and limitations of government actions. Among others, the results show the usefulness of environmental characterization studies. They serve to propose effective management and conservation measures for these fragile environments. However, whether they are incentives or regulatory measures, the tools in use have some weaknesses and flaws which keep them from being fully effective. The fate of wetlands therefore remains of concern.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".