Relating Soil Available Zinc With Physicochemical Properties In New Alluvial Zone Of West Bengal, India
Bibliographic record
Abstract
An experiment was conducted to assess the dependency of available zinc on the physicochemical properties of soils. The soil samples were collected from 15 NBSS & LUP identified soil series of New Alluvial Zone of West Bengal, India. The soil samples were processed and analyzed for different standard physicochemical properties i.e. pH, EC, clay, organic carbon content, available nitrogen, phosphorus and potassium, zinc, copper, iron, manganese, amorphous iron, aluminium and manganese oxide content. Among the studied parameters, pH, clay, organic carbon, amorphous iron and amorphous aluminium oxide showed significant correlations (-0.591*, 0.601**, 0.784**, 0.563*, 0.509* respectively) with available zinc. Multilayer Perceptron Network (MPN) in Artificial Neural Network (ANN) yielded organic carbon, clay, pH and amorphous iron content as the most important parameters to affect the availability of soil zinc. Further, using multiple linear regression modeling, it was found that changes in organic carbon and clay content together contribute 70.7% change in available zinc in soil wherein, organic carbon alone contributed to 62.4% change. Identification of crops based on their zinc requirement in appropriate textural conditions of soil as well as proper maintenance of soil organic carbon can be a promising option for judicious management of zinc in soil.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".