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Infrared Radiation Sensors of <I>Melanophila acuminata</I> (Coleoptera: Buprestidae): A Thermopneumatic Model

2005· article· en· W2051496674 on OpenAlexafffund
William G. Evans

Bibliographic record

VenueAnnals of the Entomological Society of America · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsUniversity of Alberta
FundersDirectorate for Biological SciencesUniversity of Alberta
KeywordsInfraredWavelengthMaterials scienceRadiationRadiant energyLamella (surface anatomy)OpticsOptoelectronicsPhysicsComposite material

Abstract

fetched live from OpenAlex

A description of the fine structure of the infrared radiation (IR) sensor of Melanophila acuminata (De Geer) and of the role of the wings during flight in mechanically modulating incoming IR into on and off pulses, supports a thermopneumatic model of IR perception by this insect. The model postulates that pulsed IR, corresponding to wavelengths emitted by fires, enters an enclosed, airtight, thick-walled cavity in each sensor through an apical epicuticular waveguide and is absorbed by a thin, chitinous endocuticular lamella of the cavity wall. The lamella is heated and in turn heats and expands the enclosed air that then compresses a basal cuticular cap that displaces the tubular body of the sensor, triggering a dendritic impulse and transducing an action potential in the neuron. This process is possible because of the absorption by the chitinous lamella of the wavelengths of IR emitted by fires resulting in extraordinary IR wavelength discrimination, radiant energy sensitivity, and rapid response time. These properties distinguish the sensor from temperature sensors of insects, sometimes erroneously designated as IR sensors, that respond relatively slowly to broadband wavelength radiation.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.310
Teacher spread0.259 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations23
Published2005
Admission routes2
Has abstractyes

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Same venueAnnals of the Entomological Society of AmericaSame topicNeurobiology and Insect Physiology ResearchFrench-language works237,207