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 NO 3 – after forest disturbance is commonly ascribed to a higher availability of NH 4 + to autotrophic nitrifiers, or to a reduction in available‐C resulting in lower microbial assimilation of NO 3 – . Alternatively, it has been proposed that increasing NH 4 + pools following disturbance could increase net nitrification by reducing microbial assimilation of NO 3 – . Forest floor material was collected from shelterwood harvest plots which displayed both low available‐C and low NH 4 + pools, and where previous experiments had suggested the prevalence of heterotrophic nitrification. Subsamples were amended with incremental rates of glucose‐C or NH 4 + , and gross NO 3 – transformation rates were measured by isotope dilution. Glucose‐C additions had little effect on the net difference between gross NO 3 – production and consumption rates. On the other hand, NH 4 + additions caused gross NO 3 – consumption processes to decrease sharply, while gross NO 3 – production processes remained constant. The results suggest that NH 4 + can have an immediate positive effect on net nitrification rates by suppressing NO 3 – assimilation and uptake systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".