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Record W300064660

Achieving accurate nuetron-multiplicity analysis of metals and oxides with weighted point model equations.

2024· paratext· en· W300064660 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.

Bibliographic record

VenueOSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2024
Typeparatext
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsDouglas CollegeJaneway Children's Health and Rehabilitation Centre
Fundersnot available
KeywordsPlutoniumMultiplicity (mathematics)NeutronCalibrationNuclear physicsMultiplication (music)FissionCoincidencePhysicsAnalytical Chemistry (journal)Computational physicsRadiochemistryMaterials scienceMathematicsChemistryMathematical analysisQuantum mechanics
DOInot available

Abstract

fetched live from OpenAlex

Neutron multiplicity counting is a technique for the rapid, nondestructive measurement of plutonium mass in pure and impure materials. This technique is very powerful because it uses the measured coincidence count rates to determine the sample mass without requiring a set of representative standards for calibration. Interpreting measured singles, doubles, and triples count rates using the three-parameter standard point model accurately determines plutonium mass, neutron multiplication, and the ratio of ({alpha},n) to spontaneous-fission neutrons (alpha) for oxides of moderate mass. However, underlying standard point model assumptions - including constant neutron energy and constant multiplication throughout the sample - cause significant biases for the mass, multiplication, and alpha in measurements of metal and large, dense oxides.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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