Structural differences and functional similarities between two sugar maple (Acer saccharum) stands
Bibliographic record
Abstract
The spatially inexplicit or functional multilayer models used to predict canopy transpiration or photosynthesis are based on the assumption that closed stands show less functional variability than structural variability, because foliage tends to arrange itself in space to optimize the capture of light. To validate this assumption, we compared the structural and functional properties, and the measured and modeled transpiration fluxes of two sugar maple (Acer saccharum Marsh.) stands of comparable leaf mass but differing in height and diameter distributions. One stand was characterized by a well-developed single-layer canopy, whereas the other stand had a multilayered canopy and a stem diameter distribution of the classical inverse-J shape. Stand differences in height and diameter distribution, and canopy gap fraction, were highly significant. There were minor but significant differences in leaf mass and leaf mass per unit leaf area (LMA) distributions. We found no differences in tree-level relationships between basal area and either transpiration flux or sapwood area. We compared measurements of stand transpiration with transpiration estimates obtained from a multilayer gas exchange model, in which only the nonspatial inputs, leaf area index and LMA frequency distribution described stand structure. For both stands, modeled values of daily transpiration closely followed measured values (r(2) = 0.94). These results support use of the nonspatially explicit approach to estimating canopy gas exchange, especially if the intent is to scale-up to larger portions of the landscape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".