The cleaning goby mutualism: a system without punishment, partner switching or tactile stimulation
Bibliographic record
Abstract
Abstract In the cleanerfish–client mutualism involving the Indo‐Pacific cleaner wrasseLabroides dimidiatusand its reef fish clients, mechanisms such as ‘tactile stimulation’, partner switching and punishment are used by clients to control cheating by cleaners. We sought to establish whether these behaviours are general features of cleaning mutualisms by examining their presence in interactions between Caribbean cleaning gobies (Elacatinusspp.) and their clients. The cleaning goby–client mutualism bears several similarities to the cleaner wrasse system: clients visit cleaners frequently to have their ectoparasites removed while cleaners depend heavily on these visits for food, and cheating by cleaning gobies is also prevalent. However, our data revealed striking differences between the two cleanerfish systems: clients did not seem to attempt to control cheating by cleaning gobies and cleaning gobies did not perform tactile stimulation on their clients. We suggest three hypotheses that might explain these major differences between both systems, based on differences in mutual dependence between cleaners and clients or cognitive ability of cleaners, differences in costs of being cheated and differences in foraging preferences by cleaners. Interactions betweenL. dimidiatusand its clients should probably not be seen as the ‘standard’ marine fish cleaning mutualism.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".