Considérations des effets cumulatifs pour la planification intégrée au MPO
Bibliographic record
Abstract
Integrated planning is a policy driven process to establish ecosystem, cultural, social, and economic objectives in line with a given policy. Integrated plans can provide the overarching framework for multi-jurisdictional engagement, the inclusion of community and stakeholder interests, the setting of ecological objectives informed by multi-sectoral interests, the compilation of information from different knowledge systems, and ecosystem-based and adaptive management. They are a valuable platform for incorporating cumulative effects in decision making because comprehensive assessments of cumulative effects require an understanding of the ecological impacts of human activities to inform the policies and legislation used to manage them. Integrated planning can be used to operationalize an adaptive management approach that includes monitoring the state of fish and fish habitat and compliance with regulatory actions. Synthesis of information about past and current works, undertakings, and activities (and activities not under the jurisdiction of Fisheries and Oceans Canada) within an integrated plan will position the Fish and Fish Habitat Protection Program (FFHPP) to determine the effectiveness of Departmental management and conservation strategies for fish and fish habitat and evaluate program performance. However, FFHPP will also require an integrated planning policy to establish goals for planning and the processes to implement integrated plans through regulatory decision making process regarding project proposals. The context and scope of such a policy will also help frame the science needed to inform the planning process, the monitoring activities, and the regular review of the performance of the framework, and individual integrated plans regionally. We provide and customize examples from integrated planning internationally.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".