Modeling knot geometry from scanned images of Korean pine plantations
Bibliographic record
Abstract
Based on 1038 knots from 42 sample trees from 14 standard plots in Korean pine (Pinus koraiensis Sieb. et Zucc.) plantations in Heilongjiang Province, China, we developed a knot profile model system. The concepts of growth inflection point and death inflection point were proposed. Depending on the growth conditions near the growth inflection point of the knot, we divided the knots into two types: curved knots and linear knots. A logistic regression model was constructed to predict the shape of the knots. The area under the curve of the logistic regression model was 0.699 (95% confidence interval 0.661∼0.736), and the prediction accuracy was 0.69. Our research shows that for middle-aged and young forests, the relative radial distances of the growth inflection points at the upper edge and bottom edge of curved knots were larger than those in linear knots. The curved knots did not bend at the death inflection point, but linear knots may bend at the death inflection point. Models were constructed separately for curved knots and linear knots. The results showed that the application of the mixed-effects model significantly improves the model fitting effect.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".