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Record W2163353896 · doi:10.1109/mwsym.2005.1517071

Tissue sensing adaptive radar for breast cancer detection: preliminary experimental results

2005· article· en· W2163353896 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE MTT-S International Microwave Symposium Digest, 2005. · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCancer detectionBreast cancerRadar detectionRadarBreast tumorMicrowave imagingAntenna (radio)MicrowaveComputer scienceBiomedical engineeringCancerMedicineInternal medicineTelecommunications

Abstract

fetched live from OpenAlex

Microwave breast cancer detection records the differences in electrical properties between healthy and malignant tissue. Tissue sensing adaptive radar (TSAR) has been proposed as a method for early tumor detection. Reflections from the differences in electrical properties between healthy and diseased tissue (tumors) are time-shifted and summed to create images. In this paper, the TSAR second generation experimental system is presented. An antenna is tested and characterized, reflections are recorded and the image formation algorithms are tested. Finally, preliminary tumor detection with the experimental setup is evaluated.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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