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Record W2992595197 · doi:10.1038/s41419-019-2166-0

11th Tuscany Retreat on Cancer Research and Apoptosis: Genetic profiling, resistance mechanisms and novel treatment concepts in cancer and neurodegeneration

2019· article· en· W2992595197 on OpenAlexaffabout
Justin Pogmore, Joseph Longo, Diana Resetca, Negin Farivar, Niall Buckley, María Gabriela Rincón, Christina M. Bebber, Anusha Venkatraman, James M. Pemberton

Bibliographic record

VenueCell Death and Disease · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsSunnybrook HospitalPrincess Margaret Cancer CentreUniversity of British ColumbiaUniversity Health NetworkUniversity of TorontoSunnybrook Health Science Centre
FundersMedizinische Universität WienTel Aviv UniversityUniversität Wien
KeywordsNeurodegenerationApoptosisBiologyProfiling (computer programming)CancerCancer researchBioinformaticsNeuroscienceComputational biologyMedicineGeneticsDiseasePathologyComputer science

Abstract

fetched live from OpenAlex

For the 11th time, an international assembly of scientists gathered in the medieval castle of Palazzo di Piero, near the town of Chiusi, Italy, to exchange ideas and present their research. The opening presentation was given by Brent Derry (SickKids, Canada), who showed that alternative polyadenylation regulates oncogenic Ras in both C. elegans and human cancer cells. His student, Matthew Eroglu, showcased that genes mstr-1 and mstr-2 are essential for cells to make proper fate decisions. Guy Taichman, student of Oded Rechavi (Tel Aviv University, Israel), showed that stress causes a resetting of heritable RNAi in C. elegans .

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)0.010
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0040.013
Scholarly communication0.0080.006
Open science0.0020.006
Research integrity0.0080.019
Insufficient payload (model declined to judge)0.0100.002

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 head0.034
GPT teacher head0.323
Teacher spread0.289 · 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
GenreOther

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
Published2019
Admission routes2
Has abstractyes

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