Intraspecific kleptoparasitism and counter-tactics in the archerfish (Toxotes chatareus)
Bibliographic record
Abstract
The mechanics of the archerfish’s remarkable ability to spit down aerial prey is well studied. Relatively unknown, however, are the social consequences of this hunting method. To explore how physical factors and behavioural choices affect the use and success of intraspecific kleptoparasitism in socially foraging archerfish, 10 tagged, juvenile archerfish (Toxotes chatareus) were presented in groups of 3, 5, and 7 with single crickets of 3 sizes overhanging the water by either 15 or 30 cm. Video review revealed all spits, jumps, attempted thefts, and consumptions. Kleptoparasitism attempts were common, resulting in a 43.6% loss rate to the fish that successfully brought down the prey. Group size affected the probability of kleptoparasitism asymptotically: loss rate increased as group size increased from 3 to 5 members, but with no further increase at 7 members. As observed with other kleptoparasitic species, the rate and success of kleptoparasitism increased with both prey size and prey height (analogous to handling time). Several counter-kleptoparasitism behaviours were observed, including jumping to grab prey directly, aggression, spitting technique, and positioning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".