Successful under-planting of red oak and black cherry in early-successional deciduous shelterwoods of North America
Bibliographic record
Abstract
Underplanting early-successional forest stands with red oak and black cherry was tested as a way of improving productivity on abandoned agricultural land of North American temperate deciduous forests.A partial release treatment was applied during the third growing season and compared to a control.The growth increment after six years is analyzed with respect to treatment and competition layers.Although the release treatment reduced competition at all vegetation layers, growth was mostly determined by the density of the upper layer.Deer herbivory was not increased by the release.The release treatment succeeded in significantly increasing available light for the duration of the study, while the understory recovered quickly.Planted trees, particularly red oak, responded well to the release treatment.Results substantiate the need for dynamic silviculture in sensitive, rural landscapes, where conservation of forest structure is important.under-planting / light / early-successional forests / deer herbivory / thinning treatment Résumé -Plantation sous-couvert de chêne rouge et cerisier tardif en forêt décidue pionnière d'Amérique du Nord.Des chênes rouges et cerisiers tardifs ont été introduits sous couvert de jeunes peuplements d'origine agricole dans une étude visant l'amélioration de la productivité de peuplements pionniers de la forêt décidue tempérée d'Amérique du Nord.Un dégagement partiel appliqué au cours de la troisième saison de croissance est comparé à un témoin.La croissance après six ans est analysée en fonction du traitement et des strates de compétition.Alors que le traitement avait significativement diminué la compétition à tous les niveaux, la croissance était surtout fonction de la densité de la strate supérieure.L'herbivorie par le cerf n'a pas été augmentée par le dégagement.Le traitement de dégagement a significativement accru la lumière disponible pour toute la durée de l'étude, alors que le sous-étage s'est reconstitué rapidement.Les plants, particulièrement les chênes rouges, ont bien répondu au dégagement.Les résultats supportent une sylviculture plus dynamique dans les paysages ruraux sensibles, où la conservation des structures forestières est importante.plantation sous-couvert / lumière / forêt pionnière / herbivorie par le cerf / traitement de dégagement
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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".