Early establishment of conifer recruits in the northern Rocky Mountains as a function of postfire duff depth
Bibliographic record
Abstract
Well-combusted duff (<3 cm depth) is generally considered the best seedbed for small-seeded species on upland sites, but we ask here, What is the optimal, postfire residual duff thickness? We hypothesize that a duff thickness equal to (but not greater than) the length of the germinant will offer the best conditions, because at this thickness, the duff layer will not prohibit radicle penetration into the mineral soil, and yet it will serve as a water-conserving mulch. Data from a recent fire in the Rocky mountains of British Columbia were used to show that for three species of Pinus and Picea, (1) duff depths <3 cm were far more clement substrates than thicker duff, and (2) there was a peak in relative survivorship at about 1–2 cm, somewhat shallower than the typical hypocotyl length for these species. Additional data sets from studies previously conducted at boreal and northern cordilleran sites in Alberta, Saskatchewan, Yukon, and Quebec (a combined 21 fires) bolstered these results.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".