Effect of gap size on litter decomposition and soil nitrate concentrations in a high-elevation sprucefir forest
Bibliographic record
Abstract
Possible mitigation of nitrate losses associated with clearcuts through harvesting smaller gaps was tested in a high-elevation forest of Engelmann spruce (Picea engelmannii Parry ex Engelm.) and subalpine fir (Abies lasiocarpa (Hook.) Nutt.). We measured concentrations of ammonium and nitrate after 6-week buried bag incubations of forest floor and mineral soil samples in replicated plots of uncut forest and gaps of 10, 1.0, and 0.1 ha and single-tree removal for 7 years after harvest. Nitrate concentrations in forest floor and mineral soil were elevated 37 years after harvesting in gaps of 0.1 ha and larger. Removal of the same proportion of trees as single trees did not result in increased nitrate concentrations, suggesting that nitrate losses could be reduced by harvesting single trees rather than creating gaps. Greater N availability was not associated with faster rates of decomposition of litter and forest floor, which were similar in gaps of all sizes (010 ha). Reciprocal transplant of forest floor and soil from the 10-ha gaps and the uncut forests indicated that changes in the nature of the forest floor or soil following harvest had a greater influence on nitrate concentrations than the changes in environmental conditions in the gaps.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".