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Record W3157518524 · doi:10.15173/sciential.v1i4.2406

Could a Methyl Group Predict Your Risk of Depression?

2020· article· en· W3157518524 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSciential - McMaster Undergraduate Science Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsnot available
Fundersnot available
KeywordsNeurochemicalBiomarkerDepression (economics)Serotonin transporterMajor depressive disorderDNA methylationDiagnostic biomarkerPsychologyMedicineOncologyGeneBioinformaticsPsychiatryClinical psychologyGene expressionNeuroscienceGeneticsBiologyCognitionGenotype

Abstract

fetched live from OpenAlex

Major Depressive Disorder (MDD) is a systemic condition that diminishes the daily quality of life of those affected. There are no current methods that can reliably diagnose depression on a biochemical level. The premise of this work is to report on a potential biochemical marker, DNA methylation of the serotonin transporter gene (5-HTT). This biochemical marker can serve as an indicator of gene expression patterns, ultimately leading to a neurochemical imbalance in affected individuals. Studying this biomarker has the potential to improve diagnostic and therapeutic techniques in the future, and improve the prognosis of those with MDD.

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)0.002
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 score0.063
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.280
Teacher spread0.260 · 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