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Record W4386803926 · doi:10.61450/joci.v1i3.22

Influence of Faradarmani Consciousness Field on Bacterial Population Growth

2022· article· en· W4386803926 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Scientific Journal of Cosmointel · 2022
Typearticle
Languageen
FieldMedicine
TopicBiofield Effects and Biophysics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTurbidimetryPopulationBacterial growthAntibioticsBacteriaAntibiotic resistanceMicrobiologyClinical microbiologyBiologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

The treatment of bacterial infections and the rising challenges of antibiotics resistance are global concerns and the primary topics in basic science and clinical microbiology. In the present study, the effects of treatment of selected populations of bacteria using an immaterial and non-energetic method called Faradarmani Consciousness Field (FCF) treatment are investigated. Population growth was assessed by turbidimetry, colony counting, and tetrazolium chloride reduction assays in non-treated control and Faradarmani- treated groups. Our results suggest the effect of the Faradarmani CF on reducing various types of bacterial strain growth rates (up to 46%). In addition, along with a decrease in the bacterial population, evidence of increased survival can be seen in the larger healthy population (up to about 60%). In this experiment, we confirm the effects of the Faradarmani CF on the bacterial growth population and their survival. These results suggest Faradarmani CF as a qualitative treatment, and this evidence paves the way for further investigation on TCFs. This study also warrants additional research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.249
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it