Western white pine growth relative to forest openings
Bibliographic record
Abstract
In northern Rocky Mountains moist forests, timber harvesting, fire exclusion, and an introduced stem disease have contributed to the decline in western white pine (Pinus monticola Dougl. ex D. Don) abundance (from 90% to 10% of the area). Relations between canopy openings (0.1–15 ha) and western white pine growth within different physical settings are identified. Objectives include relating western white pine seedling and sapling growth to canopy opening attributes (defined by fisheye photography), identifying western white pine competitive thresholds (occupancy, competitive advantage, free-to-grow status) in relation to opening size, and relating canopy opening attributes to overstory density descriptors. We sampled 620 western white pine plus competing trees, canopy opening characteristics, landscape position, and overstory density descriptors. Analysis included log-linear and polynomial regression. Visible sky (canopy opening) and tree age were significantly related to growth of selected pines. Radiation explained less variation in growth than canopy opening. Thresholds for western white pine to occupy a site (>23% canopy opening), gain a competitive advantage (>50% canopy opening), and achieve free-to-grow status (>92% canopy opening) over western hemlock were developed. There was a significant although weak (R2 < 0.12) relation between canopy opening and overstory density descriptors.
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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".