Natural regeneration of black spruce (Picea mariana (Mill) B.S.P.) on lowland clearcut strips near Shebandowan, Ontario / by Krzysztof Sas-Zmudzinski
Bibliographic record
Abstract
Growing conditions for black spruce natural regeneration on clearcut strips were \nstudied near Shebandowan, Ontario. These conditions were significantly different from \nthose present in the mature, residual forest in the study area. Furthermore, growing \nconditions changed with increased age of the clearcut strips. The most favourable seedbed \n(sphagnum) and limited hardwood competition were present only for few years following \nharvesting. The succession of less favourable seedbeds (sedges, hardwood litter) and \nsevere hardwood competition had a negative impact on density and stocking of spruce \nregeneration. \nHeight growth based on stem analysis data of regenerated and residual trees were \nexpressed by a Weibull function. Half of the natural regeneration consisted of advance \ngrowth. The growth progressions for both advanced growth and new regeneration \nclosely fit the growth models for the GOOD site disregarding early suppression. \nThis study demonstrates that utilizing narrow, progressive clearcut strips and \ncontrol of competition will produce a well stocked new forest as productive as the old \nforest on lowland, conifer site types.
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".