Biological Quality Indicators of a Haplortox Soil Under Different Management Systems in Southern Brazil
Bibliographic record
Abstract
The research was looking for the quick response of soil's microbiota to soil changes caused by management systems, changes to which biological indicators of soil quality are sensitive. In this context, this study aimed to evaluate the impacts of seven soil management systems, including in on hand covers of Pennisetum glaucum (PG), Avena strigosa (AS), Stizolobium aterrimum (SA), EF Pisum sativum (PS) + Avena strigosa (AS), and on the other hand, No soil tillage with gypsum application (NTSG), No soil tillage with scarification (NTSS) and No soil tillage (NTS), on soil quality bioindicators. The bioindicators recorded from top soil samples were collected from the first 10 cm at pre-planting, at pre-flowering and at post-harvest of the soybean, from July 2014 to March 2015 were Total organic carbon (TOC), Soil microbial biomass carbon (SMBC), Soil basal respiration, Respiratory coefficient (qCO2), Microbial coefficient (qMIC), and its relationship with soybean yields. The seven treatments were arranged in the field according to a completely randomized experimental, Analysis of variance (Anova) for each of the bioindicators and comparison of treatment mean values using Tukey test at 5% probability were carried out. SA, the AS+PS consortium and the no-tillage system led to significant improvement of the biological attributes of the soil. The management systems did not change the yield of soybean grain.
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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 | 0.001 | 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".