RESEARCH ON THE COEFFICIENT OF UTILIZATION OF QUERCUS PETRAEA L. LOGS FOR PRODUCING RADIAL PLANKS WITH A THICKNESS OF 33mm.
Bibliographic record
Abstract
The number of entities that sawn radial grain (quarter sawn) boards is very small. Sawing of quarter sawn logs is very complicated and it has a low coefficient of utilization.It was in the interest of entities to know utilization coefficient of logs during a quarter sawn.Quarter sawn boards primarily used for barrel branches and some other special products.In order to have reliable results are sawn 5 oak logs. Sawing of logs is done on the Beha-n entities in Rahovec, which exclusively used sawn quarter boards. Sawing of logs is done in special sawmill adapted for this purpose.Obtained data of the study:the average diameter was 34.40cm.;Average log length 50.12%;Thickness boards 33mm.Coefficient of oak log utilization in quarter sawn, expressed as a percentage:Radial boards 71.79%;Outer part with bark 19.79%;Sawdust 6.26%;Others 2.25%.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".