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Record W2170350522 · doi:10.1139/p10-062

Cold-neutron depth profiling as a research tool for the study of surface oxides on metalsSpecial Issue on Neutron Scattering in Canada.

2010· article· en· W2170350522 on OpenAlex
Z. Tun, James J. Noël, Th. Bohdanowicz, Liangzhi Cao, R. G. Downing, Lyudmila V. Goncharova

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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Physics · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of WaterlooNational Research Council CanadaWestern University
Fundersnot available
KeywordsNISTPhysicsNeutronNeutron scatteringScatteringBoronProfiling (computer programming)Neutron temperatureNuclear physicsDetectorNuclear engineeringRadiochemistryAnalytical Chemistry (journal)Environmental chemistryOpticsChemistry

Abstract

fetched live from OpenAlex

A recent experiment at NIST has demonstrated that neutron depth profiling (NDP) based on the (n, α) reaction could be developed into a tool that could be routinely used for the study of passive oxides on metals. Whereas most metals are not (n, α) active, oxides grown with 17 O, the only (n, α) active oxygen isotope, can be observed and tracked by this technique. Problems due to contamination of the samples by boron were encountered, but were shown to be surmountable. For our samples, the NDP facility at NIST, as it exists today, has enough flux and energy resolution to separate the α particles emitted by 17 O from those emitted by 10 B. Substantial improvement in the data collection rate, easily achievable with arrays of additional detectors, will make NDP a useful tool in the study of passive 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.408

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 integrity0.0000.001
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 head0.036
GPT teacher head0.308
Teacher spread0.273 · 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