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Record W6929875705 · doi:10.5061/dryad.nf1r0f6

Data from: Bacterial biophotons as non-local information carriers: species-specific spectral characteristics of a stress response

2018· dataset· en· W6929875705 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typedataset
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsLaurentian University
Fundersnot available
KeywordsBacteriaHydrogen peroxideEscherichia coliPhotomultiplierPhase (matter)Bacterial growthMicroorganismStrain (injury)

Abstract

fetched live from OpenAlex

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 works and 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 h in 5 mL of Nutrient Broth to stationary phase and cell densities of ~107 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.

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.

How this classification was reachedexpand

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.203
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0100.007
Research integrity0.0000.001
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 head0.033
GPT teacher head0.274
Teacher spread0.241 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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