Spatially explicit modeling of PAR transmission and growth of <i>Picea glauca</i> and <i>Abies balsamea</i> in the boreal forests of Alberta and Quebec
Bibliographic record
Abstract
To investigate the feasibility of a spatially explicit, radiation-based regeneration model for the boreal forest, we tested the predictions of a three-dimensional simulator of photosynthetically active radiation transmission (%PAR), MIXLIGHT, and the growth response of understory Abies balsamea (L.) Mill. (balsam fir) and Picea glauca (Moench) Voss (white spruce) to %PAR in two large (>1 ha) mixed-species forest sites, one in eastern Canada at Lac Duparquet, Quebec, and one in western Canada at Calling Lake, Alberta. Overstory tree locations and dimensions were obtained from aerial photographs or ground measurements and allometric relationships. Seasonal %PAR calculated by MIXLIGHT for the Calling Lake site was very similar to seasonal %PAR measured by quantum sensors (n = 5, %PAR range = 15%33%, r = 0.93). Daily measurements of %PAR were also predicted well by simulations at both sites (n = 3436, %PAR range = 1%45%, r ≥ 0.76). Functional relationships, designed to saturate at the maximum height growth potential of these sites, were developed to predict sapling height growth from simulated seasonal %PAR and initial height (R2 ≥ 0.74). These results demonstrate the potential of the MIXLIGHT simulator for estimating PAR at microsites within heterogeneous forests and for modeling understory tree growth.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".