Release of Naturally Established White Pine Seedlings from Competition: An Objective Field Index
Bibliographic record
Abstract
Abstract Gap cutting has been recently prescribed in Quebec to favor the regeneration of midtolerant species such as white pine. However, this new practice may lead to increased competition, jeopardizing the survival of established seedlings. One important question, therefore, is how much care will be required to ensure that the seedlings will reach the sapling stage. More importantly, a practical field tool is needed to determine under which conditions a seedling must be released from competition. For the present study, three sets of 42 white pine (Pinus strobus L.) seedlings were selected from 4-year-old gaps in a mixed tolerant hardwood–white pine stand so as to cover the widest range of competition intensity, and so the main competitor species would be individuals of one of the following: pin cherry (Prunus pensylvanica L.f.), trembling aspen (Populus tremuloides Michx.), or wild raspberry (Rubus idaeus L.). A seedling vigor index was developed, and counts of and measurements on stems of competitor species were conducted. Several competition indices were computed, and regression analyses were performed to determine which indices explained the most of the observed variation in seedling vigor. A composite competition index to be used as a decision rule to release a white pine seedling was developed by combining selected competition indices, balancing precision of the models with ease-of-use in the field. Finally, the integration and the economic assessment of the field index into the planning of regeneration efforts are presented.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".