Light attenuation by early successional plants of the boreal forest
Bibliographic record
Abstract
The influence of eight early successional plant species from the boreal forest on photosynthetically active radiation (PAR) were compared using a controlled plant competition study. Four woody (green alder, Alnus crispa (Ait.) Pursh; upland willow, Salix humilis Marsh.; white birch, Betula papyrifera Marsh.; wild red raspberry, Rubus idaeus L.) and four herbaceous (eastern bracken fern, Pteridium aquilinum L.; bluejoint grass, Calamagrostis canadensis Michx.; large-leaved aster, Aster macrophyllus L.; fireweed, Epilobium angustifolium L.) plant species were studied using an additive density experiment with jack pine (Pinus banksiana Lamb.) seedlings. The transmission of PAR through the plant canopies was measured using a line quantum sensor under six plant density treatments at the time of maximum canopy development each year. Four measures of plant abundance (planting density, actual density, projected leaf area index, and crown cover) were evaluated for their ability to predict PAR transmission through the plant canopies. Visual estimates of crown cover provided the best models each year. Vertical profiles of PAR transmission were used to compare the canopy structure among plant species and were used to refine the models. During the second growing season, increasing crown cover of bluejoint grass and large-leaved aster had the largest influence on PAR. In the third season, green alder, upland willow, and white birch (along with bluejoint grass and fireweed at the jack pine crown level) had the greatest influence on PAR. PAR measurements taken from a nearby forest for several of the plant species indicate that the models developed from our controlled experiment are reasonably applicable to naturally occurring plant populations.
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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) | 0.000 | 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".