Integrated resource management planning through the linking of mathematical and judgement-based models / by Kwang-Il Tak
Bibliographic record
Abstract
The objective of this study was to develop an \nanalytical technique to enable forest managers to handle \neffectively the complex problem of integrated resource \nmanagement planning using quantitative and qualitative \ninformation. Two different types of modelling approaches \nwere used: 1) a quantitative-oriented linear goal \nprogramming and 2) a qualitative-oriented IDA model. These \ntwo types of model were linked to complement each other. By \nmeans of an inter-disciplinary workshop approach, an attempt \nwas made to strengthen and broaden the power of the models \nto represent real world problems. Timber, wildlife and \noutdoor recreation-related objectives and variables were \nused for this study. Sibley Provincial Park in Ontario, \nCanada, was used for the trial application of this approach. \nA ten-year planning horizon and four cutting alternatives \nwere employed. A resource policy which provided all \ninterest groups in the workshop with the highest \nsatisfaction levels was developed. The forest land in the \nstudy area was allocated optimally to achieve the multiple \nobjectives of timber, wildlife and outdoor recreation. \nDetermining target levels and weights for goal programming \napplication were improved by linking LP and IDA processes. \nSubjective judgements of workshop participants were partly \nassisted and improved by initial LP solutions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".