The impact of the investment period on soil and plant pollution by cadmium and nickel in Jableh city, in Lattakia Governorate
Bibliographic record
Abstract
This research was conducted in Jableh city in the Latakia Governorate during 2019–2020 to study the level of pollution of the soils and plants of some greenhouses in Jableh city with the elements cadmium and nickel. Several greenhouses were randomly distributed in different areas in Jableh city based on the period of their investment (5, 10, 20, and 25 years), as the investment period was considered the variable factor between greenhouses. The homogeneity of greenhouse texture was taken into consideration as much as possible. Two-layer soil samples were collected (0–20 and 20–40 cm). Electrical conductivity, pH, the ratio of organic matter and the major basic elements (nitrogen, phosphorus, and potassium), and the total cadmium and nickel in the soil, plants, and cucumber fruits were determined. SPSS was used (completely randomized design). The results showed that there was pollution of greenhouse soils with the elements cadmium and nickel in a manner that is proportional to the increase in the period of investment. They also showed that the content of cadmium and nickel in cucumber fruits in the oldest houses exceeded the permissible limits. A strong positive second-degree significant (1%) correlation was observed between the available phosphorus and the total cadmium and nickel in the soil, and a strong correlation between the soil and plant content of these two elements and an increasing investment period.
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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 | 0.001 |
| 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) | 0.001 | 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".