Bacterial biophotons as non‐local information carriers: Species‐specific spectral characteristics of a stress response
Bibliographic record
Abstract
Abstract Studies by Alexander Gurwitsch in the 1920' s with onion root cells revealed the phenomenon of mitogenetic radiation . Subsequent works by Popp, Van Wijk, Quickenden, Tillbury, and Trushin have demonstrated a link between Gurwitsch's mitogenetic radiation and the biophoton, emissions of light correlated with biological processes. The present study seeks to expand upon these and other works to explore whether biophoton emissions of bacterial cultures is used as an information carrier of environmental stress. Bacterial cultures ( Escherichia coli and Serratia marcescens ) were incubated for 24 hr in 5 ml of nutrient broth to stationary phase and cell densities of ~10 7 cells/mL. Cultures of E. coli were placed upon a photomultiplier tube housed within a dark box. A second bacterial culture, either E. coli or S. marcescens , was placed in an identical dark box at a distance of 5 m and received injections of hydrogen peroxide. Spectral analyses revealed significant differences in peak frequencies of 7.2, 10.1, and 24.9 Hz in the amplitude modulation of the emitted biophoton signal with respect to whether a peroxide injection occurred or not, and whether the species receiving the injection was E. coli or S. marcescens . These and the subsequent results of discriminant functions suggest that bacteria may release biophotons as a non‐local communication system in response to stress, and that these biophotons are species specific.
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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".