Bibliographic record
Abstract
Regeneration of first-cut strips in a two-cut system of strip clear-cutting was compared to that of large clear-cutting in four different areas representative of the black spruce (Picea mariana (Mill.) BSP) stands of the boreal forest of Quebec. Seedlings were more evenly distributed in clearcut strips than in large clearcuts. Differences of about 10 000 black spruce seedlings per hectare and 20% of stocking were observed in favour of clearcut strips compared to large clearcuts. Black spruce stocking was about 14% larger on lowland than on upland sites but height growth was better on upland sites. A regeneration problem similar to that of large clearcuts was observed when the second strips were cut. One year after cutting these second strips, winter harvesting resulted in a 23% gain in black spruce stocking as compared to summer harvesting. Even if black spruce stocking marginally increased during the years following winter harvesting, the height advantage of the preserved advance growth justifies the application of this harvesting method. The strip clear-cutting system effectively improved the stocking of former black spruce stands but if the stocking level of advance growth is adequate, careful harvesting to preserve advance regeneration should be the preferred method since it would be more cost-efficient.
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 | 0.001 | 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".