Effects of experimental freezing on soil nitrogen dynamics in soils from a net nitrification gradient in a nitrogen-saturated hardwood forest ecosystem
Bibliographic record
Abstract
This study examined effects of soil freezing on N dynamics in soil along an N processing gradient within a mixed hardwood dominated watershed at Fernow Experimental Forest, West Virginia. Sites were designated as LN (low rates of N processing), ML (moderately low), MH (moderately high), and HN (high). Soils underwent three 7-day freezing treatments (0, –20, or –80 °C) in the laboratory. Responses varied between temperature treatments and along the gradient. Initial effects differed among freezing treatments for net N mineralization, but not nitrification, in soils across the gradient, generally maintained at LN < ML ≤ MH < HN for all treatments. Net N mineralization potential was higher following freezing at –20 and –80 °C than control; all were higher than at 0 °C. Net nitrification potential exhibited similar patterns. LN was an exception, with net nitrification low regardless of treatment. Freezing response of N mineralization differed greatly from that of nitrification, suggesting that soil freezing may decouple two processes of the soil N cycle that are otherwise tightly linked at our site. Results also suggest that soil freezing at temperatures commonly experienced at this site can further increase net nitrification in soils already exhibiting high nitrification from N saturation.
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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".