Performance analysis of log yards using data envelopment analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Log yards are an important component of the forest value chain. Yet, the performance of log yards, which are a specific case of warehouses, has not been thoroughly studied. The aim of this paper was to investigate log yard efficiency, explore the relationship between efficiency and operational conditions, and identify management practices that lead to best/worst performance. A benchmarking analysis of technical efficiency of 38 log yards in Quebec, Canada, was conducted by means of a Data Envelopment Analysis (DEA) approach. Three input factors (area, equipment, labor), and one output factor (annual volume), were considered in an input-oriented model. Technical efficiency scores were analyzed using a complexity factor that expressed the combined influence of seasonality, shape, and number and sort to be handled. The average technical efficiency of log yards is 61%, and 81%, when assuming constant and variable returns to scale respectively. Inefficient log yards operate under increasing returns to scale and the source of inefficiency is both technical (inefficient transformation of inputs into output) and managerial (inadequate scale of operations). The results suggest possible median reduction in input consumption of 17%, 20%, and 14%, respectively for area, equipment and labor utilization. Log yard technical inefficiency is mainly due to an inadequate utilization of area and moving equipment.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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