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Record W3003508000 · doi:10.1002/bies.201900194

Nutrient Sensing and Response Drive Developmental Progression in <i>Caenorhabditis elegans</i>

2020· review· en· W3003508000 on OpenAlexafffund
Sabih Rashid, Kim B. Pho, Hiva Mesbahi, Lesley T. MacNeil

Bibliographic record

VenueBioEssays · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsPopulation Health Research InstituteMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCaenorhabditis elegansBiologyNutrientNutrient sensingEssential nutrientDevelopmental biologyCell biologySignal transductionGeneticsEcologyGene

Abstract

fetched live from OpenAlex

In response to nutrient limitation, many animals, including Caenorhabditis elegans, slow or arrest their development. This process requires mechanisms that sense essential nutrients and induce appropriate responses. When faced with nutrient limitation, C. elegans can induce both short and long-term survival strategies, including larval arrest, decreased developmental rate, and dauer formation. To select the most advantageous strategy, information from many different sensors must be integrated into signaling pathways, including target of rapamycin (TOR) and insulin, that regulate developmental progression. Here, how nutrient information is sensed and integrated into developmental decisions that determine developmental rate and progression in C. elegans is reviewed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.286
Teacher spread0.267 · 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
GenreReview

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

Citations17
Published2020
Admission routes2
Has abstractyes

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