Vertical distribution of foliar biomass in western larch (<i>Larix occidentalis</i>)
Bibliographic record
Abstract
Western larch (Larix occidentalis Nutt.) is an endemic pioneer species in northwestern North America and unique as a deciduous conifer and the most shade-intolerant, fastest growing, and most fire-resistant species in the northwestern United States. To better understand its production ecology, we used a multilevel modeling approach to analyze the intrinsic dynamics of western larch vertical foliage distribution and compared it with other species. We found that western larch allocates foliage into a more diffuse distribution as the crown lengthens, whereas shade-tolerant evergreens concentrate foliage into a more monolayered distribution higher within the crown as it lengthens. Crown foliar biomass scaled linearly with diameter at breast height, indicating that western larch does not fill volume in the crown with foliage at an increasing rate like other conifers. Our model supports the hypothesis that foliar shade intolerance and water stress jointly influence foliage allocation in this deciduous conifer. These results also highlight intrinsic foliage distribution as a factor potentially contributing to the inability of western larch to survive light-limiting conditions and its preference for mesic sites. The models developed here provide a basic framework that may be built upon to study the morphological response of western larch to modified stand conditions such as disturbance and silvicultural treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".