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Record W4390399101 · doi:10.1088/978-0-7503-5022-8

Quantum Metrology with Photoelectrons, Volume 3

2023· book· en· W4390399101 on OpenAlexaff
Paul Hockett

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPhotoelectric effectQuantum metrologyMetrologyPython (programming language)Matrix (chemical analysis)Computer scienceQuantumWave functionSoftwareDensity matrixComputational scienceAlgorithmPhysicsQuantum computerQuantum mechanicsQuantum simulatorChemistry

Abstract

fetched live from OpenAlex

The overall aim of <i>Quantum Metrology with Photoelectrons, Volume 3</i> is to expand, explore, and illustrate new computational developments in quantum metrology with photoelectrons: specifically, the application of new Python-based tools to tackle general problems in photoionization matrix element retrieval. Part I details the topic, theory and computational methods; Part II provides further numerical details and case-studies, specifically employing the generalised bootstrap retrieval protocol, which makes use of rotational wavepackets as a geometric control parameter. Problems of various size and difficulty are investigated, with the largest for an asymmetric top with 38 complex matrix elements (equivalently, a 38x38 density matrix retrieval). Key features • Interpreting experimental data. • Extraction/reconstruction/determination of quantum mechanical properties (matrix elements, wavefunctions, density matrices) from experimental data. • Comparison of experimental and theoretical data. • New analysis methodologies and techniques. • Introduction to newly-developed software platforms.

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 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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.005

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 head0.011
GPT teacher head0.259
Teacher spread0.247 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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