Cleaning in pairs enhances honesty in male cleaning gobies
Bibliographic record
Abstract
A recent game theoretic model akin to an iterated prisoner's dilemma explored situations in which 2 individuals (the service providers) interact simultaneously with the same service recipient (the client). If providing a dishonest service pays, then each service provider may be tempted to cheat before its partner, even if cheating causes the client's departure; however, a theoretical cooperative solution also exists where both partners should reduce cheating rates. This prediction is supported by indirect measures of cheating (i.e., inferred from client responses) by pairs of Indo-Pacific bluestreak cleaner wrasses Labroides dimidiatus. Here, we examine how inspecting in pairs affects service quality in Caribbean cleaning gobies Elacatinus spp. We measured dishonesty directly by examining the stomach contents of solitary and paired individuals and calculating the ratio of scales to ectoparasites ingested. We found that the propensity to cheat of females and males differed: females always cleaned relatively honestly, whereas males cheated less when cleaning in pairs than when cleaning alone. However, overall, the cleaning service of single and paired individuals was similar. Our results confirm that cleaners cooperate when cleaning in pairs; however, our findings differ from the specific predictions of the model and the observations on L. dimidiatus. The differences may be due to differences in mating systems and cleaner–client interactions between the 2 cleaner fish species.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".