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 signifi- cant 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 5m 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. Resume : Les auteurs ont developpe et teste un modele de dissemination par le vent applique au recrutement des ar- bres dans les brulis, a partir des sources vivantes de graines en bordure du feu ou a partir des petits peuplements resi - duels epargnes par ce dernier. Le modele a aussi ete teste pour le recrutement de Pinus banksiana Lamb. dont les cones sont serotineux, a l'interieur d'un brulis. Le modele supposait que l'importance de la source est proportionnelle a la densite de la surface terriere et qu'une courbe de recrutement individuel (source ponctuelle) peut etre exprimee comme une distribution log-normale. Le modele fournissait des predictions significatives des courbes de recrutement de Picea engelmannii Parry ex Engelm., Picea glauca (Moench) Voss et Abies balsamea (L.) Mill. a des distances aussi grandes que 2,0 km, bien qu'il avait tendance a sous-evaluer les predictions dans le cas d' Abies et a surevaluer dans le cas de Picea. Le modele a fourni d'importantes predictions concernant le recrutement de Pinus banksiana a l'interieur des brulis a partir des graines provenant des banques aeriennes et, dans le cas de P. glauca et de Larix laricina (Du Roi) K. Koch, jusqu'a 100 m des peuplements residuels. Selon les standards de la foresterie, les brulis sont pauvres en especes qui doivent obligatoirement etre recrutees a partir des bordures. Dans le cas des grands feux, la densite relative adequate pour une espece telle que l'epinette blanche ayant une surface terriere de 5 m 2 a l'hectare, serait limitee a en- viron 70 m de la bordure. On s'attend a ce que les petits peuplements residuels fournissent a l'avenir environ la moitie des recrues d'epinette blanche et de sapin a des distances excedant environ 800 m a partir d'une bordure fictive du brulis. (Traduit par la Redaction) Greene and Johnson 1274
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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; both teacher heads agree on what is shown here.
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".