Influence of Faradarmani Consciousness Field on Antibiotics Resistance in Bacteria
Bibliographic record
Abstract
The development of antibiotics resistance arising from antibiotic over-treatment is the major challenge in eliminating harmful bacteria and is associated with grave financial and human consequences worldwide. The contraction of resistant bacteria from hospitals is a key concern, and many scientific research fields aim to develop strategies that prevent bacterial resistance to antibiotics. Taheri Consciousness Fields, as novel Fields, were founded and introduced by Mohammad Ali Taheri. These Fields are neither matter nor energy, therefore, they cannot be measured directly. But it is possible to study their effects on objects through controlled experiments. After investigating the effect of Faradarmani Consciousness Field on bacterial populations in a previous study, we aimed to investigate the effect of Faradarmani CF on antibiotic resistance of bacteria in identified hospital strains. As confirmed by disk diffusion and MIC methods, we found that resistance in the bacterial populations was altered. Specifically, Pseudomonas.aeruginosa, Escherichia. coli, Bacillus.subtilis, Klebsiella.pneumoniae, Acinetobacter.bummani, and Staphylococcus.aureus strains showed a decrease in antibiotics resistance sensitivity, while S.aureus and P.aeruginosa strains showed an increase in sensitivity to antibiotics. Based on the results, Faradarmani CF has the ability to affect antibiotics resistance response in resistant populations. We suggest that this observation requires further attention. In the event the observations can be replicated by other researchers, Faradarmani CF could be considered an effective solution to this global issue.
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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) | 0.002 | 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".