Predicting crown class in three western conifer species
Bibliographic record
Abstract
Assessing the crown class (dominant, codominant, intermediate, suppressed) of a tree is a subjective procedure. Most definitions of crown class are based on the relative height of a tree and (or) the amount of light that is incident on the tree crown. With this research, we devised a classification scheme, based on easily measured tree variables, to assign a crown class to trees. Our data consisted of tree measurements, including crown class, from four stem-mapped 0.05-ha sample plots with buffers. The light model tRAYci was used to assess the light incident on each tree crown. These data gave us field-based and light-based assessments of crown class. The classification and regression tree technique with diameter at breast height (DBH), height, relative DBH, relative height, height to the crown base, and crown depth as variables was used to classify the trees. Accuracy rates of 91% and 82% were achieved for the field-based and light-based assessments of crown class, respectively.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".