Nitrogen mineralization and nitrification in a mixed-conifer forest in southern California: controlling factors, fluxes, and nitrogen fertilization response at a high and low nitrogen deposition site
Bibliographic record
Abstract
Net fluxes of nitrogen (N) mineralization and nitrification were measured in situ on a monthly basis for 3 years at a high (HN) and low (LN) N deposition site in the San Bernardino Mountains, California. Mean N mineralization fluxes in the forest floor and top 10 cm of mineral soil were 19.0 and 59.8 kg N·ha–1·year–1 at LN and HN, respectively. Mean net nitrification fluxes were 11.2 and 55.9 kg N·ha–1·year–1 at LN and HN, respectively. Relative nitrification (the percent N mineralized that was nitrified) was generally lower under Pinus ponderosa Dougl. ex P. & C. Laws. (or Pinus jeffreyi Grev. & Balf.) canopies than under Quercus kelloggii Newb. or open canopies. The rate of net N mineralization was the key factor for predicting the rate of net nitrification. Fertilization with 50 and 150 kg N·ha–1 at LN significantly increased the rates of net mineralization and net nitrification. At HN fertilization had no significant effect on net nitrification. We conclude that at low-deposition sites increased nitrification occurs in the short term in response to added N, but that sustained elevated net nitrification is driven by the accumulation of N-enriched litter and soil organic matter in conjunction with chronic throughfall N deposition inputs.
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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 | 0.001 | 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 | 0.000 |
| 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".