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Record W1535337979

Discrimination Between Phonograph Playback Systems

2011· article· en· W1535337979 on OpenAlex
Jason Hockman, David M. Weigl, Catherine Guastavino, Ichiro Fujinaga

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

VenueJournal of the Audio Engineering Society · 2011
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhonographDigitizationSet (abstract data type)Computer scienceVariety (cybernetics)Active listeningComponent (thermodynamics)Process (computing)EngineeringTelecommunicationsArtificial intelligencePsychologyElectrical engineeringCommunicationOperating system
DOInot available

Abstract

fetched live from OpenAlex

Digitization of phonograph records is an important step towards the preservation of our cultural history and heritage. The phonograph playback systems (PPS) required for this digitization process are comprised of several components in a variety of price ranges. We report on the results of two listening tests intended to ascertain the extent to which expert listeners can discriminate between PPS of different price ranges. These results are intended to determine the extent to which component selection affects the discrimination between PPS and to provide a set of guidelines for the purchase of PPS components for the digitization of phonograph record collections.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.258

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 science0.0010.000
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 head0.027
GPT teacher head0.199
Teacher spread0.171 · 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