Soil nitrogen mineralization and enzymatic activities in fire and fire surrogate treatments in California
Bibliographic record
Abstract
Miesel, J. R., Boerner, R. E. J. and Skinner, C. N. 2011. Soil nitrogen mineralization and enzymatic activities in fire and fire surrogate treatments in California. Can. J. Soil Sci. 91: 935–946. Forest thinning and prescribed fire are management strategies used to reduce hazardous fuel loads and catastrophic wildfires in western mixed-conifer forests. We evaluated effects of thinning (Thin) and prescribed fire (Burn), alone and in combination (Thin+Burn), on N transformations and microbial enzyme activities relative to an untreated control (Control) at 1 and 3 yr following treatment in northern California. N mineralization and net nitrification were reduced by Thin and by Burn in year 1, and N mineralization was increased by Thin+Burn in year 3, relative to the Control. In general, all experimental treatments reduced soil enzyme activity. To identify overall treatment effects on the below-ground ecosystem, we combined these data with soil physicochemical data from this site to perform non-metric multidimensional scaling (NMS) ordination. NMS ordination showed that Burn and Thin+Burn produced the greatest overall effects on soil, and that overall differences in soil characteristics among treatments diminish over time. These results provide an important benchmark for monitoring ecosystem effects of large-scale wildfire hazard reduction strategies over the long term.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".