Effects of neighbours on crown length of Abies lasiocarpa and Picea engelmannii in two old-growth stands in British Columbia
Bibliographic record
Abstract
Crown length is a key aspect of vertical structure in multi-aged, multistrata, mixed-species forests. Crown length, defined as the distance from the tree top to the lowest live branch whorl, was determined for 3169 mapped trees ≥4.0 cm in diameter, in four 0.25 ha plots in each of two old-growth Abies lasiocarpa (Hook.) Nutt. – Picea engelmannii Parry ex Engelm. stands. We randomly selected half the trees to build models and half for validation. Crown length was modelled as a function of tree height, incorporating modifiers to account for neighbouring trees. The inclusion radius and height threshold for competition were 3 m and 70%, respectively. The addition of two modifiers significantly improved the model for A. lasiocarpa; crown length decreased as the number of neighbours increased and as the height of surrounding trees increased. In contrast, none of the modifiers significantly improved the model for P. engelmannii. Except at high levels of competition, Abies crowns were longer than those of Picea. However, both species in these forests have long crowns, typically three-quarters of the tree height and rarely less than half. The tightly programmed conical crown architecture of these species likely contributes to long crowns and to the limited effects of neighbours on crown length.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".