Microbial biomass, nitrogen and phosphorus mineralization, and mesofauna in boreal conifer and deciduous forest floors following partial and clear-cut harvesting
Bibliographic record
Abstract
The effects of partial and clear-cut harvesting on forest floor physical, chemical, and biological properties, forest floor mesofauna, and nutrient cycling were investigated in conifer- and deciduous-dominated stands of Alberta's mixedwood boreal forest. Forest floor samples were collected 2.5 years after harvest from clearcuts, strip-cut corridors in a partial cut, green tree retention patches in a partial cut, and uncut control sites. Partial cuts showed intermediate decreases in annual litter input and NH 4 -N between uncut and clear-cut sites of both the deciduous and conifer stands, as did microbial biomass, PO 4 -P, mesofauna abundance (total, Acari, and Collembola), and fine root biomass in the conifer stands. In the deciduous stands, microbial biomass and fine root biomass in partial- and clear-cut treatments were not significantly different, but were significantly reduced compared with the uncut controls. Mesofauna abundance was reduced in the corridors of the partial-cut treatment compared with partial-cut patch, clear-cut, and uncut treatments. In both deciduous and conifer stands, NO 3 -N was elevated in the partial-cut corridors and clearcuts compared with partial-cut patch and uncut treatments. Findings from this study show that negative impact to forest floor properties associated with clear-cut harvesting may be reduced in partial-cut harvesting systems.
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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 | 0.000 | 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) | 0.000 | 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".