Bibliographic record
Abstract
Resource managers, planners, and the public are unified in their calls for monitoring of land-use plans. Unfortunately, many monitoring initiatives fall short of their potential for several reasons: indicators are not explicitly linked to objectives, hindering feedback to planning; knowledge is not represented in a manner that facilitates learning; and monitoring priorities are driven subjectively. We describe a framework that links indicators to existing objectives, presenting knowledge as hypotheses about the probability of achieving an objective as a function of various indicator levels. Uncertainty is explicitly included in the models. The framework can be used for management decision support and to prioritize objectives for implementation, effectiveness, and validation monitoring, and research. Monitoring priority is determined first by probability of success and uncertainty and then by the importance of an objective. We present a case study for the Babine Watershed, an area in the interior of British Columbia with high resource values and decades of controversy and ineffective monitoring. The framework sifted through existing objectives to focus effort on those most critical to monitor. By concentrating on publicly derived, regionally applicable objectives and strategies taken from existing land-use plans, the framework provided relevant results and enabled rapid feedback.
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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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".