Zastosowanie metody granicznej analizy danych do oceny efektywności gospodarowania w leśnictwie i przemyśle drzewnym
Bibliographic record
Abstract
The paper presents the literature review concerning the general assumptions of the Data Envelopment Analysis (DEA) and its applications in forest management and wood industry evaluation. So far, efficiency evaluation in forestry sector was carried out using mostly the ratio analysis only. The DEA approach, which was developed in the 1970s, is based on the mathematical programming algorithm and gives more opportunities to analyze efficiency in forestry sector. We describe the main steps of DEA and possible options for the analysis. It also discusses the advantages and disadvantages of the method applications. We also present the most significant papers concerning DEA application in forest management and wood−based industry. As far as forest management is regarded, DEA was used for evaluating forest offices, administration and institutions and optimizing forestry operations performed by both private and state−owned companies. In case of wood−based industry, the method was used mostly for evaluating efficiency of sawmills and pulp mills. Most of the research in this field was carried out in the United States and Canada. DEA was applied to measure efficiency in forest management in Poland only recently, which should be considered as a good step towards improvement of research quality in this field in Poland and should provide comprehensive results of forest management efficiency evaluation. The method should be used more widely to evaluate efficiency of various aspects of forestry and wood−based industry in Poland.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".