Microbiological characterization of land set-aside before and after Roundup desiccation
Bibliographic record
Abstract
Luvic chernozem (set-aside from 1996) was evaluated. The first period, before Roundup desiccation (2002-2003), was characterized by black, spontaneous and controlled fallows (mowed with the removal of plant biomass or mulched); the following period (2003-2004) by black fallow with repeated Roundup applications; and the last period (2004-2006) involved re-grassing by a mowed Lolium multiflorum Lam. monoculture. The characterization included microbial biomass, available organic carbon, basal respiration, metabolic quotient, biomass-specific available organic carbon, arylsulfatase activity, soil organic matter carbon and total nitrogen. Mulching of pure cultures of grasses and legumes contributed to a high soil organic matter accumulation. Repeated Roundup desiccation caused a strong (highly significant) decrease of arylsulfatase activity (-28%), however highly significant increase of microbial biomass (+69%) and nitrate-nitrogen (+86%) were determined. The subsequent re-grassing compensated the changes described. The soil biological properties were best preserved on mulched fallow with Lotus corniculatus L. and Festuca pratensis L., also in regard to contamination with weeds.
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 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 | 0.000 |
| 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) | 0.001 | 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".