Total Iron and Different Iron Forms Contents Affecting by Soil Characteristics in Vertisols, SE Turkey
Bibliographic record
Abstract
Abstract Iron (Fe) in the soil is a very important element for agricultural applications and the development of plants, which have different forms. The presence of Fe in the rhizosphere is controlled by the activity of Fe forms in the soil and the microorganisms and surrounding root-soil interaction of plants and roots. The objectives of this study were to identify, and to examine their interaction with soil properties in vertisols. The results showed that; FeT contents ranged from 1.17 and 47.71 g kg-1, mean 13.81 g kg-1 › FeD contents 0.18 to 17.85 g kg-1 and mean 7.23 g kg-1 › FeO contents 0.01–0.31 g kg-1 mean 0.18 g kg-1 › FeDTPA contents 0.00–0.17 g kg-1 mean 0.04 g kg-1 › FeP contents 0.00–0.02 g kg-1 mean 0.01 g kg-1. Clay-silt fractions and organic matter had a very powerful impression on Fe forms distribution. FeD, FeDTPA, FeO ve FeP had low quantities. This is thought to be due to insufficient rainfall and some soil characteristics (including high pH, low organic matter, clay texture, and high lime content), as iron is not easily dissolved in the soil. There was a very important relation between clay content, organic matter, silt fractions, and Fe forms in the studied area. There was a positive correlation between soil organic carbon and DTPA-extractable Fe. There was a negative correlation between DTPA-extractable Fe and soil pH, also calcium carbonate content. When total Fe was rised, clay-silt content and cation exchange capacity (CEC) increased in the soil profiles. Especially in Zone 3 has more Fe and Fe contents, the results showed that; There was a very good balance between the different Fe fractions and soil properties. Zone 3 had more total iron contents than the other zones, because of their soil properties. Fe bound by organic sites, water-soluble plus exchangeable Fe and Fe were adsorbed onto oxides (amorphous surfaces) and were positively correlated with the DTPA-extractable Fe.
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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".