Picea glauca dynamics and spatial pattern of seedlings regeneration along a chronosequence in the mixedwood section of the boreal forest
Bibliographic record
Abstract
We studied Picea glauca dynamics in the boreal forest of Saskatchewan, Canada, using 35 stands ranging from < 1 to > 200 y after fire. We determined the spatial pattern and the importance of seedbed conditions to the recruitment of P. glauca. Basal area increased along the chronosequence peaking at 110 y after fire (51.5 m2 ha–1). The ratio of softwood to hardwood increased from 0.03 (16 y) to 17.0 (172 y). Picea glauca tree density increased with stand age, highest densities were observed in a 172 y stand (1413 stems ha–1). Picea glauca dominated the canopy between 93 and 172 y after fire. Picea glauca snags appeared about 66 y after fire, and remained relatively low in density until 160 y. Saplings were present at varying densities along the chronosequence. Seedlings established immediately after fire and exhibited bimodality with lowest densities observed between 110 and 125 y. Analysis with Ripley’s K, showed that seedlings were mainly distributed at random in young stands but were clumped at a distances < 12 m in old stands. In young stands, the majority of seedlings regenerated on the organic layer (LFH 73%), while recruitment was almost exclusively on logs in old stands (94%). Picea glauca regeneration depended on propagule availability and seedbed characteristics at early stand age. Logs and the resultant canopy gaps formed, appear to be critical for P. glauca regeneration in mature and old stands.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".