Production ecology of<i>Thuja occidentalis</i>
Bibliographic record
Abstract
Equations to predict branch and tree leaf area, foliar mass, and stemwood volume were developed from 25 destructively sampled northern white-cedar ( Thuja occidentalis L.) trees, a species whose production ecology has not been studied. Resulting models were applied to a large sample of 296 cored trees from 60 sites stratified across a soil gradient throughout northern Maine. Nonlinear regression analysis was used to assess alternative forms of the relationship between volume increment (VINC) and projected leaf area (PLA); analysis of covariance was used compare stemwood growth efficiency (GE) among soil-site classes, light exposure classes, and the presence of decay. Stem volume was estimated with Honer’s equation ( T.G. Honer. 1967. Forest Management Research and Services Institute ) with refitted parameters. PLA was best predicted with Maguire and Bennett’s nonlinear model ( D.A. Maguire and W.S. Bennett. 1996. Can. J. For. Res. 26: 1991–2005 ) using sapwood area or crown length and the ratio of tree height to diameter at breast height. A sigmoid model form captured the relationship between VINC and PLA more precisely and with less bias than the simple power function; this implies that the relationship between GE and PLA reaches a peak rather than decreases monotonically. At PLAs >50 m2, GE gradually declined with increasing crown size and was significantly influenced by site and light exposure. With PLA, site, and light held constant, decayed trees had a significantly lower (by 11%) GE than sound stems, a finding not previously reported for other tree species.
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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".