Patterns of leaf area and growing space efficiency in young even-aged and multiaged coast redwood stands
Bibliographic record
Abstract
Projected leaf area estimates were used to predict volume increment and basal area of second-growth coast redwood ( Sequoia sempervirens (D. Don) Endl.) trees on Jackson Demonstration State Forest, Mendocino County, California. Sample plots were established within even-aged and multiaged mixed-species stands. Redwood tree basal area growth was more strongly related to sapwood area than to tree size and differed significantly between canopy strata and overstory stratum crown classes. Projected leaf area was predicted from sapwood area for each tree, and summarized to the stand level, giving a maximum stand leaf area index (LAI) estimate of 14.9 m 2 /m 2 . Redwood tree growing space efficiency (GSE; the ratio of stem volume increment to leaf area) was greatest on average among emergent overstory trees, followed by dominant and codominant overstory trees. There was no evidence of declining overstory tree GSE with increasing leaf area over the range of data collected. A nonlinear model predicted increasing understory tree GSE with increasing leaf area. Models that predict basal area and LAI were developed to permit implementation of GSE models from basic inventory data.
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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 | 0.001 |
| 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) | 0.000 | 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".