Bibliographic record
Abstract
The title of this opinion article is a homage to Professor Hameroff’s paper of 2012 entitled “How quantum brain biology can rescue conscious free will” where the role of quantum computations in microtubules inside brain neurons is described as it relates to the elusive concept of free will [1]. Quantum biology, and its applications in medicine, represents a fascinating new field of research where “spooky” (as in Einstein’s definition of entanglement, “spooky action at a distance”) phenomena occur. However, rather than being spooky, these apparently inexplicable phenomena may confer protection against stressors and, more in general, against diseases. In a very recent paper, researchers from the McMaster and Queen’s Universities of Canada, described results related to radiation-induced bystander effect that can be explained only by taking into consideration quantum biological processes at the level of complex organisms such as rainbow trout or zebra fish [2]. In this study, the Authors observed that both types of fish were able to anticipate a biological response to events that had not yet occurred, as if some form of entanglement between fish exposed or nonexposed to ionizing radiations had taken place. Interestingly, these biological responses had a protective meaning as if the irradiated fish “warned” the non-irradiated animals to take precautions against a harmful event. In layman’s words, the irradiated fish had experienced the harmful effects of an environmental stressors, had adapted accordingly by implementing protective responses, and then, showing a remarkable display of altruism, had “shared”, in ways that still have to be described, this bad experience with the non-irradiated fish so that they could be prepared and protected just in case the experimenters decided to irradiate them. Quite obviously, the non-irradiated fish who had learned from the bad experience of the irradiated ones, have now a significant advantage; should they be irradiated, they are protected.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".