A different kind of smart: What pathologists can learn from an octopus
Bibliographic record
Abstract
I n the Oscar-winning documentary My Octopus Teacher, filmmaker Craig Foster forms an unlikely bond with a curious and intelligent octopus in a kelp forest just off the coast of South Africa.One of the film's major themes is how connection with other living things, even ones that perceive the world far differently than we do, can alter our own perception.First with anecdotal evidence and then through eye-opening research results, octopuses have forced us to reconsider our notions of what intelligence actually means.Pathologists likewise have had to contend with a manifestation of fluid intelligence that can spread and metastasize.Cancer has a kind of rudimentary but devilishly effective form of intelligence that we do not fully understand, and "outthinking" it may require some out-of-the-box considerations and varying perspectives on how the same general problem might be solved in vastly different ways.In that regard, the octopus may be an effective teacher.In her 2019 article entitled "What Is in an Octopus's Mind?," Jennifer Mather, PhD, a professor of psychology at the University of Lethbridge in Alberta, Canada, argues that the animal may have a different "way of being" in the world. 1 Even so, she writes, it has a mind that is capable of exploring to acquire information, calculating what to do with the world around it, and using flexible problem-solving to avoid threats from predators or other octopuses.If
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".