The effects of silvicultural disturbances on cryptogam diversity in the boreal-mixedwood forest
Bibliographic record
Abstract
In northern forests, cryptogams (spore producing plants) occupy a key position in forest ecosystem diversity and function. Forest harvesting and silvicultural practices have the potential to reduce cryptogam diversity. This project uses four blocks that were mechanically site prepared, planted with a single conifer species, and subsequently subjected to five conifer release treatments: (1) motor-manual cleaning, (2) mechanical brush cutting, (3) aerial application of triclopyr, (4) aerial application of glyphosate, and (5) control (untreated clearcut). Five 10 × 10 m subplots were installed in each of the five treatment plots and the uncut forest on the four blocks. Botanical surveys were conducted before and 15 years after treatments. Species richness and abundance, Shannon's and Heip's indices, and rank abundance diagrams clearly show that richness and abundance were affected by silvicultural treatments. Vegetation management treatments resulted in significant reductions in cryptogam diversity, to the point that only a few colonists and drought-tolerant species remained. Cryptogam diversity was ranked in the following order: forest > clearcut > mechanical clearing > herbicide treatment. Herbicide treatments had the greatest initial effect on species richness, species abundance, and diversity indices. Cryptogam diversity showed signs of recovery 5 years after treatments. Missed strips (untreated areas) within a clearcut provided a refuge for remnant communities of forest cryptogams that could play a key role in the rehabilitation forest diversity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".