Modelling outer- and inner-crown profiles based on tree status and cardinal directions in relation to competition for <i>Larix kaempferi</i> plantations in northeastern China
Bibliographic record
Abstract
Using the branch data from 90 sample trees, we developed novel models of outer- and inner-crown profiles for the northern, eastern, southern, and western sides of planted Larix kaempferi (Lam.) Carrière trees in northeastern China via the integration of competition indices (CIs) based on nonlinear marginal regression (NMR). We also used nonparametric boundary regression (NBR) to model the crown-profile boundary. The largest crown radius and inflection points of sample trees were calculated using NBR and NMR. We determined that the CIs of the ratio of the diameter of the subject tree to the quadratic mean diameter (CI3) and ratio of the basal area of the subject tree to the mean basal area of the stand (CI5) were the best distance-independent CIs for incorporation into the models of outer- and inner-crown profiles, respectively. The CIs showed a significant effect on the outer-crown profile in all four directions for dominant, intermediate, and suppressed trees but did not show a significant effect on inner-crown profile. The outer-crown radius increased and inner-crown radius decreased with increasing CI3 and CI5, respectively. The crown profile on the northern side was the largest, which conformed to the regularity in the mean current increment of the sampled branches.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".