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Record W3122290629 · doi:10.1287/msom.2020.0923

NetEase Cloud Music Data

2020· article· en· W3122290629 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

VenueManufacturing & Service Operations Management · 2020
Typearticle
Languageen
FieldComputer Science
TopicMobile Crowdsensing and Crowdsourcing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCloud computingSample (material)ImpressionComputer scienceRevenueSet (abstract data type)Data setPhoneWorld Wide WebAdvertisingBusinessArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes the impression/display data and corresponding user, creator, and music content card data from NetEase Cloud Music. This data set is collectively supplied by the Revenue Management and Pricing (RMP) section of INFORMS and NetEase Cloud Music to support data-driven research in operations management. The data contain more than 57 million impressions/displays of music content cards recommended to a random sample of [Formula: see text] users from November 1, 2019 to November 30, 2019. For each impression, the data provide the corresponding user activities, such as clicks, likes, and follows. Moreover, the data set also contains information on each user, each content creator, and each content in the impression sample.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.804
Threshold uncertainty score0.869

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 communication0.0010.001
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.055
GPT teacher head0.240
Teacher spread0.185 · 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