Octopus. The ocean’s intelligent invertebrate.
Bibliographic record
Abstract
This book, with its elegant appearance, including the wonderful cover picture showing the "intelligent" glance of the octopus, represents a nice and timely overview on the cognitive capabilities of this amazing cephalopod species. It is authored by Jennifer A. Mather, a leading scientist on animal personality, professor at the University of Lethbridge in Alberta, where she teaches a variety of human and social psychology courses. The second author, Roland C. Anderson, is a Seattle-based biologist; while the last one, James B. Wood, is an outreach expert in a major aquarium and an accomplished underwater photographer. The relevance of this book for public health in general resides primarily in a major change occurring on these days in European legislation on the protection of animals used in experimental procedures. Following in fact a norm implemented in the UK already several years ago (the Animals Act Order 1993), the new Directive states in Article 1 (3b) that live cephalopods are among the animals falling within the scope of the Directive. This legislative step follows the awareness raised by cognitive experiments performed with octopus, which showed unexpected cognitive abilities (among these studies, we want to remember the studies by Graziano Fiorito at the Stazione Zoologica Anton Dohrn, in Naples). Generally speaking, remarkable cognitive abilities are thought to be linked with the possibility of experiencing significant level of sufferance, including psychological suffering. These considerations have moved European politicians to embrace the British norm at European level. Therefore, experimenters willing to carry out studies on octopus, cuttlefish or squids will soon have to provide written justification to motivate their experiments, while veterinarians will have to evaluate psychophysical suffering of these molluscs. In sum, despite a few points where the narrative style resembles the one of a coffee-table book, this volume remains a nice contribution to a hot topic issue that has seen invertebrate species among sentient animal deserving special protection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".