Net effect of competing vegetation on selected environmental conditions and performance of four spruce seedling stock sizes after eight years in Québec (Canada)
Bibliographic record
Abstract
- A study was established in 1993 to determine the response of four black spruce (Picea mariana) and white spruce (P. glauca) stock sizes on two sites located in Québec (Canada), each representing a different type of competing vegetation. At each site, a split-split-plot design with 15 to 17 replicates was used, in which the presence of competition (weedy and bare plots), seedling initial size, and spruce species were assigned respectively to the whole plot, the subplot, and the sub-subplot. Larger initial seedling size provided a greater competitive ability for light and had higher growth rates than the standard stock size for both species. Growth gains from combining plantation of large stock with vegetation control were multiplicative. Non-crop vegetation significantly lowered the seasonal profile of 10-cm depth soil temperature on both sites. This study shows that early release treatment is required on sites dominated by raspberry-hardwood competition complex and planting large spruce stock on such harsh competition sites will help reduce the need for repeated vegetation control.
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 | 0.000 | 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 | 0.001 | 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".