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Record W3022113082 · doi:10.4039/tce.2020.22

Dissection of<i>Anoplophora glabripennis</i>(Coleoptera: Cerambycidae) larval tissues for physiological and molecular studies

2020· article· en· W3022113082 on OpenAlexaff
Alex S. Torson, Lauren E. Des Marteaux, Susan Bowman, Meng Lei Zhang, Kevin Ong, Daniel Doucet, Brent J. Sinclair, Amanda D. Roe

Bibliographic record

VenueThe Canadian Entomologist · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceWestern University
Fundersnot available
KeywordsLonghorn beetleHindgutBiologyMalpighian tubule systemLarvaMidgutHemolymphDissection (medical)AnatomyZoologyBotany

Abstract

fetched live from OpenAlex

Abstract Many biological processes are partitioned among organs and tissues, necessitating tissue-specific or organ-specific analysis (particularly for comparative -omics studies). Standardised techniques for tissue identification and dissection are therefore imperative for comparing among studies. Here we describe dissection protocols for isolating six key tissues/organs from larvae of the Asian longhorned beetle,Anoplophora glabripennis(Motschulsky) (Coleoptera: Cerambycidae): the supraoesophageal ganglion, posterior midgut, hindgut, Malpighian tubules, fat body, and thoracic muscle. We also describe how to extract haemolymph and preserve whole larvae for measurements such as protein, lipid, and carbohydrate content. We include dissection protocols for both fresh-killed and previously frozen specimens. Although this protocol is developed forA. glabripennis, it should allow standardised tissue collection from larvae of other cerambycids and be readily transferrable to other beetle taxa with similar larval morphology.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.269
Teacher spread0.234 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

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