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Record W8761288

The Impact of the Suburbanization of Employment on Transit Modal Share: A Toronto Region Case Study

2013· article· en· W8761288 on OpenAlexaboutno aff
Pamela S. Jewell, Eve Wyatt

Bibliographic record

VenueTransportation Research Board 92nd Annual MeetingTransportation Research Board · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsSuburbanizationTRIPS architectureModalWork (physics)Mode choiceTransit (satellite)Transport engineeringPublic transportBusinessGeographyEngineering
DOInot available

Abstract

fetched live from OpenAlex

Recent evidence obtained by cytogenetic and molecular studies indicates that in breast cancer chromosome 6q is often affected by genetic changes suggesting the existence of putative tumor suppressor genes (TSGs). However the function of gene(s) on this chromosome in breast cancer suppression is not understood. To substantiate further the presence of breast cancer related TSGs at 6q and to define their location, we first performed microcell-mediated transfer of chromosome 6 to CAL51 breast cancer cells for studying possible suppression of malignant phenotype and secondly, we analysed DNAs from 46 primary breast cancers for loss of constitutive heterozygosity (LOH) using 24 poly-morphic microsatellite markers. The chromosome transfer resulted in loss of tumorigenicity and reversion of other neoplastic properties of the microcell hybrids. Polymorphism analysis of single hybrids revealed that they harbored only a small donor chromosome fragment defined by the marker D6S310 (6q23.3-q25) and flanked by D6S292 and D6S311. The LOH data suggest that four tumor suppressor gene loci mapped to the central and distal portion of 6q may be independently deleted in breast cancer. One of these regions corresponds to the region identified by chromosome transfer.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0030.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.427
Teacher spread0.347 · 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 designObservational
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

Citations2
Published2013
Admission routes1
Has abstractyes

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