Bibliographic record
Abstract
We developed and tested a wind-dispersal model of tree recruitment into burns from living sources at the fire edge or from small unburned residual stands. The model was also tested on recruitment of serotinous Pinus banksiana Lamb. within a burn. The model assumed that source strength is proportional to basal area density and that an individual (point source) recruitment curve can be expressed as a lognormal distribution. The model made significant predictions of the recruitment curves of Engelmann spruce (Picea engelmannii Parry ex Engelm.), white spruce (Picea glauca (Moench) Voss), and balsam fir (Abies balsamea (L.) Mill.) to distances as great as 2.0 km, although it tended to underpredict Abies and overpredict Picea. The model gave significant prediction of recruitment for jack pine (Pinus banksiana) within burns with seeds derived from aerial seed banks, and of white spruce and tamarck (Larix laricina (Du Roi) K. Koch) up to 100 m from residual stands. By forestry standards, burns are poorly stocked by those species that must obligately recruit from edges. In large fires, adequate stocking by a species such as white spruce that had 5 m 2 /ha of basal area would be limited to about 70 m from the edge. Small residual stands are expected to supply about half of all the recruits of white spruce or fir at distances exceeding about 800 m from a nominal burn edge.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".