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Record W6967413031 · doi:10.5061/dryad.pg4f4qrq2

Ant predation towards termite and treehopper baits in the Ecuadorian Amazon

2021· dataset· en· W6967413031 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPredationHoneydewMutualism (biology)Amazon rainforestEntomologyPredator

Abstract

fetched live from OpenAlex

Animals may develop mutualistic associations with other species, whereby prey offer resources or services in exchange for protection from predators. Alternatively, prey may offer resources or services directly to their would-be predators in exchange for their lives. The latter may be the case of hemipterans that engage in mutualistic interactions with ants by offering a honeydew reward. We test the extent to which a honeydew offering vs. partner recognition may play a role as proximate mechanisms deterring ants from predating upon their hemipteran partners. We show that, when presented with a choice between a hemipteran partner and an alternative prey type, mutualist ants were less likely to attack and more likely to remain probing their hemipteran partners. This occurred even in the absence of an immediate sugary reward, suggesting either an evolved or learned partner recognition response. To a similar extent, however, ants were also less likely to attack the alternative prey type when laced with honey, even after the honey had been depleted, suggesting an ability of ants to recognize new potential sources of honey. Either possibility suggests a degree of innate or learned partner recognition.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.106
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.035
GPT teacher head0.261
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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Same venueZenodo (CERN European Organization for Nuclear Research)→French-language works237,207→