Shake it off: exploring drivers and outcomes of autotomy in marine invertebrates
Bibliographic record
Abstract
Autotomy refers to self-amputation where the loss of a limb or organ is generally said to be (1) in response to stressful external stimuli; (2) voluntary and nervously mediated; (3) supported by adaptive features that increase efficiency and simultaneously mediate the cost; and (4) morphologically delineated by a predictable breakage plane. It is estimated that this phenomenon has evolved independently nine different times across the animal kingdom, appearing in many different taxa, including vertebrate and invertebrate as well as aquatic and terrestrial animals. Marine invertebrates use this behaviour in a diversity of manners that have yet to be globally reviewed and critically examined. Here, published data from marine invertebrate taxa were used to explore instances of injury as an evolutionary driver of autotomy. Findings suggest that phyla (e.g. Echinodermata and Arthropoda) possibly experiencing high rates of injury (tissue damage or loss) are more likely to be able to perform autotomy. Additionally, this review looks at various morphological, physiological and environmental conditions that have either driven the evolution or maintained the behaviour of autotomy in marine invertebrates. Finally, the use of autotomic abilities in the development of more sustainable and less ecologically invasive fisheries is explored.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".