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Record W6948121224 · doi:10.5061/dryad.2z34tmpkv

Coral degradation impairs learning of non-predators by Whitetail damselfish

2021· dataset· en· W6948121224 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsContext (archaeology)PopulationStaringNektonFeature (linguistics)

Abstract

fetched live from OpenAlex

A prerequisite for effective antipredator responses is the ability of the prey to distinguish animals that pose a threat from those that do not. Prey often have efficient learning mechanisms to learn threats but learning to recognize nonpredators may be equally or more important. Moreover, the ability to generalize learned information is of key importance for prey animals. Prey take information they know about one species to make ‘educated guesses’ about the predatory/nonpredatory status of other unknown species. Here, we investigate the ability of Whitetail damselfish (Pomacentrus chrysurus) to learn the identity of non-predators and then generalize their responses to other unknown animals. Our work is completed within the context of unprecedented habitat degradation in reef ecosystems. When corals die, the remaining skeleton is colonized by algae, cyanobacteria and sessile invertebrates. These opportunistic colonists change the physical and chemical landscape of the reef and hence the background odour in which predator and non-predator recognition occurs. Our results indicated that Whitetail damselfish (Pomacentrus chrysurus) learn to classify Moonwrasse (Thalasomma lunare) as a non-predator through the process of latent inhibition, whereby the prey are repeatedly exposed to Moonwrasse odour multiple times in the absence of negative reinforcement. These fish subsequently generalized their nonpredator recognition to other unknown wrasse, but not distantly related fish. Of key importance was our finding that the patterns and extent of non-predator learning and generalization were dramatically altered in dead coral habitats. As predicted, prey that learned the Moonwrasse as a nonpredator in live coral environments did not subsequently respond to Moonwrasse when we tried to teach them Moonwrasse was a predator in live coral. However, this non-predator recognition was reduced in dead coral environments. Moreover, generalization completely failed when we changed from live to dead coral environments. Juvenile damselfishes need to rapidly catalogue the identity of unknown animals when they arrive at a reef. Changing background odours, that occur with changing tides and currents, means that prey need to learn non-predator identities separately in each water source. This cognitive challenge likely has significant survival consequence in a changing environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.050
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.025
GPT teacher head0.269
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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