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Record W7009749604

A forward genetic screen for enhancers of «ksr-1» lethality

2010· other· en· W7009749604 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2010
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsnot available
Fundersnot available
KeywordsGenetic screenEnhancerGeneAlleleChromosomeSuppressorPhenotypeSynthetic lethality
DOInot available

Abstract

fetched live from OpenAlex

Kinase suppressor of Ras (KSR) is a scaffold protein that is required to localize components of the Ras/ERK pathway to the plasma membrane. In C. elegans, two genes, ksr-1 and ksr-2, encode KSR proteins which are redundantly required for development. Less than 1% of ksr-1(-) animals die in L1 larval stage in a distinct rod-like manner, but additional loss of ksr-2 or other positive regulators of the pathway enhance the rod-like lethal phenotype. To identify addition genes that might function with KSR, we performed a forward genetic screen to isolate genes that enhance ksr-1 rod-like lethality. From the screen, six enhancers were isolated, three of which are alleles of an E2 ubiquitin conjugating enzyme, ubc-25. Additionally, three alleles of an unknown gene on chromosome X were isolated but are not enhancers of ksr-1 rod-like lethality. These new enhancers as well as the mutations on chromosome X might define new regulators of Ras/ERK signaling.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.003
GPT teacher head0.158
Teacher spread0.155 · 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
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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueLibrary and Archives Canada (Government of Canada)→Same topicGenetics, Aging, and Longevity in Model Organisms→French-language works237,207→