Comparison of four measures designed for assessing the fit between the demand and output distributions of logs
Bibliographic record
Abstract
In recent years, as customer-oriented production strategies have gained ground, especially in the sawmill industry, the fit between the demand and the actual output for logs of different sizes and qualities has become an important criterion for evaluating bucking outcomes. In this paper, we present four measures for determining the similarity between the demand and output log distributions: (1) the apportionment degree, (2) the χ2 statistic, (3) Laspeyres' quantity index, and (4) the price-weighted apportionment degree. The potential of each measure for determining similarity was analyzed in two ways. (1) In an experiment involving 10 artificial Norway spruce (Picea abies (L.) Karst.) stands and two demand matrices for spruce logs, each study stand was harvested using a bucking simulator and the resulting log distributions were then compared with the desired distribution in each of the four measures. (2) The advantages and disadvantages of the measures were analyzed in relation to the requirements for an ideal goodness-of-fit measure. Both analyses suggested that not one of the four measures tested is superior, but that all can be used in actual wood procurement.
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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 | 0.023 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".