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Record W1964768935 · doi:10.1016/j.ijrobp.2013.09.040

Current Status and Recommendations for the Future of Research, Teaching, and Testing in the Biological Sciences of Radiation Oncology: Report of the American Society for Radiation Oncology Cancer Biology/Radiation Biology Task Force, Executive Summary

2013· article· en· W1964768935 on OpenAlexaff
Paul E. Wallner, Mitchell S. Anscher, Christopher A. Barker, M. Bassetti, Robert G. Bristow, Adam P. Dicker, Silvia C. Formenti, Edward E. Graves, Stephen M. Hahn, Tom K. Hei, Alec C. Kimmelman, David G. Kirsch, Kevin R. Kozak, Theodore S. Lawrence, Brian Marples, William H. McBride, Ross B. Mikkelsen, Catherine C. Park, Joanne B. Weidhaas, Anthony L. Zietman, Michael L. Steinberg

Bibliographic record

VenueInternational Journal of Radiation Oncology*Biology*Physics · 2013
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Cancer Institute
KeywordsRadiation oncologyMedicineSpecialtyMedical physicsMedical educationOncologyEngineering ethicsRadiation therapyFamily medicineInternal medicineEngineering

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.124
metaresearch head score (Gemma)0.132
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.876
Threshold uncertainty score0.653

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1240.132
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.008
Science and technology studies0.0040.005
Scholarly communication0.0170.014
Open science0.0080.005
Research integrity0.0220.019
Insufficient payload (model declined to judge)0.0210.007

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 head0.064
GPT teacher head0.480
Teacher spread0.416 · 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.

Study designNot applicable
DomainMethods
GenreCommentary

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

Citations28
Published2013
Admission routes1
Has abstractno

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