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Record W4383059127 · doi:10.1101/pdb.prot108160

Dissection and Immunolabeling of the Central and Peripheral Nervous System of<i>Drosophila</i>Larvae

2023· article· en· W4383059127 on OpenAlexaff
Katherine Clayworth, Mary Gilbert, Vanessa J. Auld

Bibliographic record

VenueCold Spring Harbor Protocols · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImmunolabelingDrosophila melanogasterBiologyCentral nervous systemNervous systemPeripheral nervous systemContext (archaeology)AnatomyDrosophila (subgenus)LarvaNervous tissueCell biologyNeuroscienceImmunologyImmunohistochemistryGeneticsGeneBotany

Abstract

fetched live from OpenAlex

The ability to visualize the cells and proteins of a tissue within their original context (i.e., in vivo) is invaluable for the study of that biological system. Visualization is especially important in tissues with complex and convoluted structures, such as the neurons and glia of the nervous system. The central and peripheral nervous systems (CNS and PNS, respectively) of the third-instar larvae of the fruit fly, Drosophila melanogaster , are found on the ventral side of the larvae and are overlaid by the rest of the body tissues. Careful removal of overlying tissues while not damaging the delicate structures of the CNS and PNS is essential for proper visualization of these tissues. This protocol describes the dissection of Drosophila third-instar larvae into fillets and their subsequent immunolabeling to visualize endogenously tagged or antibody-labeled proteins and tissues in the fly CNS and PNS.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.005

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 head0.028
GPT teacher head0.282
Teacher spread0.255 · 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
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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

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