Quantification of Nitrogen Reductase and Nitrite Reductase Genes in Soil of Thinned and Clear-Cut Douglas-Fir Stands by Using Real-Time PCR
Bibliographic record
Abstract
The abundance of nifH, nirS, and nirK gene fragments involved in nitrogen (N) fixation and denitrification in thinned second-growth Douglas-fir (Pseudotsuga menziesii subsp. menziesii [Mirb.] Franco) forest soil was investigated by using quantitative real-time PCR. Prokaryotic N cycling is an important aspect of N availability in forest soil. The abundance of universal nifH, Azotobacter sp.-specific nifH (nifH-g1), nirS, and nirK gene fragments in unthinned control and 30, 90, and 100% thinning treatments were compared at two long-term research sites on Vancouver Island, Canada. The soil was analyzed for organic matter (OM), total carbon (C), total N, NH₄-N, NO₃-N, and phosphorus (P). The soil horizon accounted for the greatest variation in nutrient status, followed by the site location. The 30% thinning treatment was associated with significantly greater nifH-g1 abundance than the control treatment in one site; at the same site, nirS in the mineral soil horizon was significantly reduced by thinning. The abundance of nirS genes significantly correlated with the abundance of nirK genes. In addition, significant correlations were observed between nifH-g1 abundance and C and N in the organic horizon and between nirS and nirK and N in the mineral horizon. Overall, no clear influence of tree thinning on nifH, nirS, and nirK was observed. However, soil OM, C, and N were found to significantly influence N-cycling gene abundance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".