The development of a timber allocation model using data envelopment analysis
Why this work is in the frame
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Bibliographic record
Abstract
A timber allocation model using data envelopment analysis (DEA) was developed to test the capability of DEA to assist with multicriteria timber allocation decisions. The resulting DEA Timber Allocation model is able to allocate forest stands, referred to as stewardship units, to different forest products companies without the need for weighing or prioritizing the allocation criteria. The allocation procedure was demonstrated in a case considering two allocation criteria: profit and employment. The allocation generated by the model was compared with random, profit-based, and employment-based allocations. The results showed that the model was capable of producing practical solutions and balancing the two allocation criteria. However, adding other allocation criteria was complicated by procedural concerns. Despite its current limitations, the model opens the door to future applications of DEA in forest resource allocation problems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it