Soil N and C transformations in two forest clear-cuts during three years after mounding and inverting
Bibliographic record
Abstract
Transformations of nitrogen and carbon in the humus layer were studied in central Finland on two clear-cut forest sites for 3 yr after spot mounding and inverting together with an untreated control. In laboratory-incubation experiments, samples from the humus layer were measured for rates of C mineralization, net N mineralization and net nitrification, and the amounts of C and N in the microbial biomass were determined. Soil solutions were collected for 4 yr with suction lysimeters. Overall, site preparation did not affect the rate of net N mineralization significantly (per kg organic matter). In the first growing season, however, the mean rate of net N mineralization was higher in both site preparation treatments than in the untreated control. At first, net nitrification was negligible in all treatments, but in the second year it increased in both site preparation treatments. On the other site, C mineralization was lowest with inverting, but the amounts of microbial C and N were highest. This indicates a shortage of easily mineralizable C sources in inverted spots. In the soil solution collected below the humus layer, increased concentrations of NO3-N and total N were found in both site-preparation treatments but, during the fourth year, overall concentrations of N had declined. Key words: Boreal forest, clear-cut, N transformations, site preparation, soil solution
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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 | 0.001 | 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".