THE CHEMICAL QUALITY EVALUATION OF SOME SOILS (CHERNOZEMS and LUVISOLS) FROM DOLJ COUNTY
Bibliographic record
Abstract
Agricultural lands represent 79% of the surface of Dolj county and about 84% are used as arable crops. Depending on the culture system chosen, the physical and chemical quality of the soil can be affected in different ways. The chemical quality of some soils from Dolj county (Chernozems, Luvisols) was evaluated using several indicators (soil reaction, organic matter content, total nitrogen, mobile phosphorus and mobile potassium). On the depth of 0-50 cm, in both soils, 75% of the mobile phosphorus values were very low-low, respectively extremely low-low. Most of the studied Luvisols were characterized by low values of the mobile potassium content (50% in the topsoil and 63% on the 0-50cm depth, respectively). High correlation between PAL and KAL content was found in case of Luvisols (R2=0.908) and low in case of Chernozems (R2=0.300). High correlation in case of Luvisols may be due to the applied fertilizers.The values of the content of organic matter, total nitrogen, mobile phosphorus and mobile potassium were lower in the case of Luvisols compared to Chernozems and in most cases the values of the studied chemical indicators decrease with an increasing depth. According to the analyzed data, these soils have high potential for mineral ang organic fertiliser application.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".