The Effect of Gabbroic Rock on Vegetative Growth, and Nutrient Status of Sesame
Bibliographic record
Abstract
The paper aims at evaluation of finely crushed gabbro from Sinai, Egypt as a cheap soil conditioner to increase food crops production. Gabbro from different localities was collected and characteristic using XRD and XRF. The results from XRD analysis of samples of gabbro from Wadies Nesreen, Saal, El Akdar and Ferian revealed the abundance of amphibole (hornblende Ca (Mg, Fe, Al) (Al, Si)8O22(OH)2, actinolite {Ca2} {Mg4.5-2.5Fe0.5-2.5} (Si8O22) (OH)) and calcic-plagioclase (labrador-bytownite) as the main components. Quartz, clinochlor and mica are recognized in addition to olivine and pyroxene. XRF data revealed the abundance of a number of macro- micronutrients that are essential for plant growth (notably calcium, magnesium, and trace elements: iron, manganese, zinc, and copper) and relatively low amounts of phosphorus and potassium. The average of the sum of the 4 basic cations (Ca, Mg, K, and Na in cmol/kg of crushed gabbro) provided a sound basis for determining the effective cation exchange capacity (ECEC) of the fraction. The cultivation of sesame in the saline sandy clay loam soil without gabbroic rock was failed. This failure attributed to the sensitivity of sesame to salinity. The height of sesame plants cultivated with ground gabbro excited 5 feet tall which revealed high fertility and high moisture content. This fertility could be attributed to the ground gabbroic rock as soil conditioner.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".