Seeing orange: prawns tap into a pre-existing sensory bias of the Trinidadian guppy
Bibliographic record
Abstract
Sensory bias, a predisposition towards certain signals, has been implicated in the origin of mate preferences in some species. A risk associated with these biases is that they can be co-opted by predators as sensory lures. Here we propose that the orange spots on the brown pincers of a diurnal, predatory species of prawn function as lures for Trinidadian guppies, which have a sensory bias for orange. We exposed female guppies to (i) a life-like model of this Trinidadian prawn with orange, green or no spots on the pincers or (ii) a live, novel (non-Trinidadian) crustacean (crayfish), also with spotted pincers. First, we provide evidence that guppies sympatric with the prawn recognized our model as a potential predator. Next, we found that guppies spent more time in the dangerous head region of the model prawn with orange-spotted pincers compared with unspotted pincers. Finally, we show that allopatric, but not sympatric, guppies spent more time in the vicinity of the head of a live crayfish when orange spots were added to its pincers than when brown spots were added. Our results suggest that the orange spots on prawn pincers can act as a sensory lure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".