Does Competition for Clients Increase Service Quality in Cleaning Gobies?
Bibliographic record
Abstract
Abstract In a biological market, members of one trading class try to outbid each other to gain access to the most valuable partners. Competition within class can thus force individuals to trade goods or services more cheaply, ultimately resulting in conflict (e.g. cheating) over the value of commodities. Cleaning symbioses among fish appear to be good examples of biological markets. However, the existence and effect of outbidding competition among either types of traders (cleaners or clients) have never been tested. We examined whether increasing competition among cleaning gobies (Elacatinusspp.) for access to clients results in outbidding in the form of provision of a better cleaning service. On reefs where fish clients visited cleaning stations less frequently, and thus competition among cleaners was higher, cleaning gobies ingested fewer scales relative to the number of ingested parasites, i.e. they cleaned more honestly. This shift in cleaner behaviour towards greater honesty is consistent with a greater market value of access to clients in the face of competition among cleaners. However, this pattern could have also arisen as a result of differences in ectoparasite availability across reefs and therefore in value of the commodity offered by clients. Experimental manipulations will be required to determine whether cleaning service quality by cleaning gobies was enhanced solely because of competitive outbidding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".