Quantification of the Carbon Content of the Fractions of Humic Substances and Total Organic Carbon in Different Production Systems
Bibliographic record
Abstract
Soil organic matter is degraded and easily altered by the type of management. The objective of this work is to determine the total organic carbon and humic substance fractions in the organic matter of the soil with different management types and depths in the western region of Paraná, Brazil. The work was carried out in the Experimental Farm “Professor Antônio Carlos dos Santos Pessoa”, belonging to the State University of the West of Paraná. Five soil management systems were evaluated: one area with corn cultivation for silage (CS); other area with succession of crops, with soybean in summer and corn in winter (SC); the next area also with succession of crops, with soy in the summer and oat in the winter (SO); the following area with permanent pasture with Tifton (PP); and the last area with crop-livestock integration (ILC). For each management system, four plots were randomly selected, in each plot three simple samples were collected in a diagonal direction to form a composite sample for the depth of 0.00-0.05 m, 0.05-0.10 m and 0.10-0.15 m. Total organic carbon, fractionation of the humic substances and the AH/AF and EA/HUM ratios were calculated. For most of the analyzed variables, it was verified that there were significant differences (P < 0.05) between the systems evaluated in the studied depths. In the evaluated areas, the PP, SO and ILC systems presented the highest carbon content for all attributes analyzed.
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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".