Developing crown shape model considering a novel competition index — a case for Korean pine plantation in Northeast China
Bibliographic record
Abstract
Effect of neighbor tree competition on crown shape of Korean pine plantation in Northeast China was studied. A total of 48 trees aged 7–58 years were felled, and all living branches were measured. A novel neighbor competition index that considered the overlap length between subject tree and neighbor tree crowns was created and incorporated into crown shape model. The effects of neighboring competition on crown shape, largest crown radius, and inflection point were analyzed. A dummy variable approach was used to detect crown asymmetry. The crown length index (CLI) of the subject tree was selected as the best neighbor competition index, and the mean square error reduction compared with that of the basic model was 2.0% after incorporating CLI. Crown radius displayed differences in the four cardinal directions and followed the order north > west > south > east. The crown radius in four directions for different ages decreased with increasing CLI and the difference increased with increasing tree age. The competition index, which considers horizontal and vertical competition effects, can significantly improve the performance of crown shape model. Both of inflection point and largest crown radius increased with increasing of CLI; however, the Pearson correlation coefficient was not significant ( P > 0.05).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".