British Columbia's Coastal Forests Variable Retention Decision Aid for Biodiversity and Habitat Retention
Bibliographic record
Abstract
Variable retention (VR) refers to a strategy that is designed to retain biological legacies, such as large old trees, snags, and downed logs, at harvest to create and/or maintain structurally complex stands with a range of silvicultural systems. The retention system is a new silvicultural system (Forest Practices Code – Operational and Site Planning Regulations) designed for use under a VR strategy (Mitchell and Beese 2002). By retaining certain structural elements, habitat carrying capacity can be maintained and connectivity can be conserved over the landscape. The planning and implementation of VR is a complex process, with many potential risks that must be understood if one is to successfully achieve multiple management objectives. With the implementation of the retention system in coastal British Columbia, researchers have generated much information and learned many lessons. This Stand Establishment Decision Aid (SEDA) is intended to provide general guidance and points to consider when implementing the various structures (aggregated or dispersed) that are associated with the retention system in British Columbia's coastal forests. Additional information related to retention and variable retention can be found in the Resource and Reference list at the end of this document. It is important to note that the list provided in this reference section is not exhaustive and more information is available, but not necessarily cited. Reference material that is not available on-line can be ordered through libraries or the Queen's Printer at: http://www.qp.gov.bc.ca.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 0.010 |
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".