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Record W2008804228 · doi:10.1145/2588555.2594518

SerpentTI

2014· article· en· W2008804228 on OpenAlexaff
Alex Cheng, Mary Malit, Chuanxi Zhang, Nick Koudas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocial mediaComputer scienceWorld Wide WebFocus (optics)AnalyticsTheme (computing)Social network serviceService (business)Data scienceBusiness

Abstract

fetched live from OpenAlex

Pinterest is a pinboard style photo sharing web service that allows its users to manage, share and express their interests via a collection of theme based photos. A few design choices of Pinterest makes it highly desirable to social media practitioners and marketers as a new, high quality data source for deep analysis, or as a complimentary data stream to existing social data such as Twitter and Facebook. The analysis capabilities at the current Pinterest site are minimal however as the focus is currently on user experience. We provide a description of SerpentTI, a system that currently crawls, indexes and aggregates more than 31 million users, 96 million boards and 3.1 billion pins from Pinterest to enable flexible and deep analytics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.100
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1000.099

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 head0.017
GPT teacher head0.282
Teacher spread0.265 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

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

Citations3
Published2014
Admission routes1
Has abstractyes

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