The Effect of Heavy Pollutants on Plant Immunity and the Spread of Fungal and Bacterial Diseases: A Study on Iraqi Palm Trees in the Brick Factories Area in Nahrawan
Bibliographic record
Abstract
This work has been done in order to study the effect of factory pollution on the concentration of arsenic in the soil and plants with special reference to palm trees.Fungi and bacteria were isolated from infected tissues of palm trees in this regard, by using appropriate culture medium Potato Dextrose Agar (PDA) for fungi and Nutrient Agar (NA) for bacteria.Due to the two types of isolations, for this reason, a test PCR was conducted which identified the species that caused the pathogenic disease.The activities involved included soil and plant sampling, isolation of organisms, culturing of the isolates on suitable medium.Determination of pH and organic matter content was done as well as measurement of root growth and leaf number to assess the soil quality.It was noted that there was a significant increase in arsenic concentration within the soil and plants around the factory while the pH and organic matter content was vice-versa.The isolated microorganisms were three fungal species, namely, Fusarium oxysporum, Aspergillus niger, and Rhizoctonia solani, and two bacteria that included Pseudomonas aeruginosa and Bacillus subtilis, with an associated positive isolation rate of 80% and 90%, respectively.The success rate of amplification of the target species was 93.3% as described by the results of the PCR.Our finding suggests that pollution from the factory is harmful to the quality of the soil and plant growth since it enhances the concentration of arsenic in the soil and harms palm health.The results also portray the fact that pathogenic organisms are present and they are more harmful to palm growth than bacteria.Moreover, the application of molecular diagnostics serves as an effective tool in the identification of pathogens that help in enhancing the health of crops and protection of palms from incidences of diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".