Relationship between intelligence and spectral characteristics of brain biophoton emission: Correlation does not automatically imply causation
Bibliographic record
Abstract
Despite enormous efforts, any correlation between “intelligence” and cognitive or physiological/anatomical properties of animal brains is still poorly understood because intelligence depends on multiple factors in parallel and not a single determinant (1). The study by Wang et al. (2) is both novel and intriguing, but we believe that the experimental results presented do not directly support their conclusion. Unfortunately, a mechanistic explanation of the correlation between intelligence and the spectral properties of biophotonic emission is not given in ref. 2, raising concerns that the observed correlation does not reflect a causal relationship but rather an accidental coincidence. Based on the … [↵][1]1To whom correspondence should be addressed. Email: jackt{at}ualberta.ca. [1]: #xref-corresp-1-1
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.023 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".