An alternative explanation for the post‐disturbance NO<sub>3</sub><sup>–</sup> flush in some forest ecosystems
Bibliographic record
Abstract
The appearance of soil NO3– after forest disturbance is commonly ascribed to a higher availability of NH4+ to autotrophic nitrifiers, or to a reduction in available‐C resulting in lower microbial assimilation of NO3–. Alternatively, it has been proposed that increasing NH4+ pools following disturbance could increase net nitrification by reducing microbial assimilation of NO3–. Forest floor material was collected from shelterwood harvest plots which displayed both low available‐C and low NH4+ pools, and where previous experiments had suggested the prevalence of heterotrophic nitrification. Subsamples were amended with incremental rates of glucose‐C or NH4+, and gross NO3– transformation rates were measured by isotope dilution. Glucose‐C additions had little effect on the net difference between gross NO3– production and consumption rates. On the other hand, NH4+ additions caused gross NO3– consumption processes to decrease sharply, while gross NO3– production processes remained constant. The results suggest that NH4+ can have an immediate positive effect on net nitrification rates by suppressing NO3– assimilation and uptake systems.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".