Predictive equations for leaf area and biomass for sugar bushes in eastern Ontario
Bibliographic record
Abstract
In January 1998, an extensive ice storm caused severe damage to sugar bushes in Eastern Ontario. Foliage biomass and foliage area estimates were required to assess effects of the ice storm and remedial treatments on variables related to sugar maple production. Equations were developed to predict leaf biomass of undamaged individual sugar maple trees in the ice-damaged area. The data were collected in early to mid-August 2000 in eastern Ontario. Basal diameter of all third-order branches of 22 trees from two stands was measured, along with tree DBH, total height, and height to the base of live crown. In addition, foliage was collected from two branches (one from the lower and one from the upper part of each tree's crown). Samples were used to develop equations predicting leaf biomass (oven-dried weight) of individual branches from their basal diameter. These equations were applied to estimate total leaf biomass of individual trees, and the resulting estimates were used to develop equations predicting leaf biomass from DBH and the number of branches per tree. The resulting equations accounted for over 90% of the variation in leaf biomass of individual trees. Leaf biomass-DBH equations for the two stands were significantly different (P < 0.0001), while no significant difference was detected in the leaf biomass-number of branches equations (P = 0.1573) for the two stands. Key words: allometric equation, branch basal diameter, DBH, ice damage, leaf area, leaf biomass, sugar maple (Acer saccharum Marsh.)
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".