Numerical characteristics of live, grade, and quarter methods of log sawing. Subject of study.
Bibliographic record
Abstract
The subject of the study is the influence of the method of sawing the log on the averaged parameters of the cut boards. The aim of the study. The aim of this study is to calculate the numerical characteristics of the live, grade, and quarter methods of log sawing. The width and the angle between the annual layer and the face of the board are considered as parameters of the boards, and the numerical characteristics of these parameters of the boards, generalized for the log, are considered as parameters specific to the method of its sawing. Research methods. The research was carried out by the method of mathematical modeling. The following simplifications are applied: only boards that are located within the cylindrical part of the log are taken into account; wood losses in cuts are not taken into account; the thickness of the boards is considered to be much smaller than the diameter of the end of the log, and there are no restrictions on their width. The calculation of the average value of the width of the boards that are cut from the log is carried out taking into account part of the area of the end of the board in the area of the top end of the log. Results of work. For the studied methods of sawing logs, the average value (relative to the top end diameter) and the coefficient of variation of the width of the boards, as well as the average value and the coefficient of variation of the angle between the annual layer and the face of the board are calculated. For example, the live method is characterized by the following parameters: the average width of the boards is 85% of the top diameter, while the coefficient of variation of their width is 20%; the average angle between the annual layer and the face of the board is 45 with an angle variation coefficient of 58%. A promising direction of further studies is taking into account the influence of the minimum width of the boards on the characteristics of the methods of sawing the log.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".