Tree recruitment in gaps of various size, clearcuts and undisturbed mixed forest of interior British
Bibliographic record
Abstract
Tree seedling recruitment was monitored after various types of logging in mixed conifer and deciduous forests of northern British Columbia, Canada. Predicting tree seedling recruitment after disturbance is fundamental to understanding forest dynamics and succession and is vital for forest management purposes. Seedling recruitment success in multi-species northern latitude forests varied as a function of mature tree canopy cover, gap size and position in a gap. Recruitment was abundant within canopy gaps across a wide range of gap sizes (20–5000 m2), but recruit numbers dropped off rapidly under the closed forest canopy and in the open conditions of clearcuts. Inside canopy gaps, recruitment was similar by gap position in small gaps (<300 m2) but, in these northern latitude forests, exhibited a trend of increasing density from the sunny north to shady south end of larger gaps. This was true for all tree species regardless of their shade tolerance ranking. There was no evidence of gap partitioning by any of the tree species during the regeneration phase suggesting that adaptation to the subtleties of gap size during early recruitment are not well developed in these tree species. Favorable locations for emergence and early establishment of germinants were less favorable for growth and survival of established seedlings, i.e. the regeneration niches in these forests were discordant. Tree abundance and species diversity appears to be controlled more by differentiation among growth and survival niches than by the regeneration niches. From the perspective of forest management, abundant natural regeneration of all the dominant tree species of these mixed-species forests can be obtained after partial cutting.
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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 | 0.001 |
| 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".