HOW WELL DOES BORDERING FOREST COMPOSITION PREDICT TREE ESTABLISHMENT IN UTILITY CORRIDORS?
Bibliographic record
Abstract
The relation between bordering tree forest composition, seed rain and tree seedling establishment was estimated in a powerline corridor of southern Quebec (Canada).Red maple was the most important tree species in the bordering forests, accounting for nearly half of the 18 tree species found, in terms of basal area.Seeds from 10 tree species were found in the seed traps, the large majority of which being from gray birch, followed by red maple, paper birch, and white ash.A good proportion of seeds from gray birch and eastern hemlock were dispersed in winter.Seedlings from 14 tree species were sampled in the right-of-way, the large majority being from red maple and gray birch.The relation between nearby bordering tree, seed and seedling abundances varied among tree species.Seed abundance collected within a single year appeared to be poorly related to tree representation and seedling abundance, suggesting high interannual variation in seed production.Distance to seed source was weakly related to seedling establishment for some species such as gray birch.However, for many species, tree composition in the neighboring forest was a good predictor of nearby seedling establishment, implying that dispersal limitation may be an important factor in determining tree composition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".