The effects of silvicultural disturbances on cryptogam diversity in the boreal-mixedwood forest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it