THE ART AND SCIENCE OF SUSTAINABLE RESOURCE MANAGEMENT PLANNING SCIENCE FORUM PROCEEDINGS
Bibliographic record
Abstract
What can be more challenging and rewarding than contributing knowledge or developing plans for the management of British Columbia’s natural resources? How can you consider and manage for achieving objectives at various scales and for different values? How can managers combine what they learn from the latest science and indigenous knowledge with the tools and knowledge acquired through years of experience? Why is it important to consider both art and science in developing sustainable resource management plans? How can scientists best contribute their knowledge to sustainability solutions?Forrex, in partnership with the Forest Investment Account–Forest Science Program (fia–fsp), hosted this Science Forum to support exploration of the art and science of sustainable forest management planning. The Forum aimed to:• increase awareness of the challenges, the art, and thescience of sustainable forest management planning;• increase awareness of current projects and initiativesthat are contributing knowledge to improve scienceand knowledge-based resource managementplanning; and• stimulate dialogue between resource professionalsengaged in science and resource managementplanning.Forrex and fia–fsp appreciate the contributions of presenters and registrants, of session Chairs, and of the Forum organizing committee. We are pleased to present the following package of Popular Summaries from some of the presentations and posters as an enduring record of the Forum’s dialogue. Due to certain constraints, not all summaries are presented here. We encourage readers to contact authors of those presentations for more information.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.086 | 0.012 |
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".