Effect of the Tillage System on the Properties of Humic Acids of Soil of the Kujawy Region in Poland
Bibliographic record
Abstract
The aim of this study has been to determine the properties of humic acids of soil depending on the tillage system applied. The study covered the soil where plough tillage, strip-till and ploughless tillage were used, and so the systems which differed completely in the way they affect the post-harvest residue, “plant residue management”. From averaged samples of soil humic acids (HAs) were extracted to identify their elemental composition, spectrometric properties for the UV-VIS and IR range as well as hydrophilic-hydrophobic properties. With the results one can conclude that the humic acids of the soil under plough tillage show a lower degree of humification as compared with HAs of ploughless tillage, whereas the parameters recorded for HAs of the soil with strip-till point to the similarity to HAs of soil with plough tillage and ploughless tillage. With that in mind, with some approximation, the degree of humification (maturity) of HAs can be ordered as follows: HAs with plough tillage < HAs with strip-till < HAs with ploughless tillage. Thus one can conclude that the tillage method combines two primary objectives; ensuring conditions favourable to plant growth and development and the effort to maintain the possibly highest humus stability.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".