MétaCan
Menu
Back to cohort
Record W2241072933 · doi:10.5281/zenodo.27876

ITensor: Extensible IndexType, Quieter Makefiles, Bugfixes

2015· article· en· W2241072933 on OpenAlexaff
Miles, Jessica Alfonsi, Andrey E. Antipov, Guy Cohen, Lucas O. Wagner, SDepenbrock, Kyungmin Lee, Erik Schnetter, jgukelberger

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsPerimeter Institute
Fundersnot available
KeywordsComputer scienceExtensibilityComputer securityBusinessOperating system

Abstract

fetched live from OpenAlex

IndexType now a class instead of an enum; this makes IndexType's user extensible, see commit a92c17a56 Makefiles now hide most compiler output for a nicer installation experience Bugfixes include: AutoMPO now works correctly independently of order of operators passed Updates to CMakeLists.txt files for cmake users (thanks Andrey Antipov) Added MSVC version of mkdtemp for Windows users (thanks Kyungmin Lee) Fixed bug in hams/TriHeisenberg.h (thanks Hitesh Changlani)

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)0.005
metaresearch head score (Gemma)0.027
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: Software
Teacher disagreement score0.233
Threshold uncertainty score0.779

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.027
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0040.010
Open science0.0050.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.2330.175

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 head0.278
GPT teacher head0.356
Teacher spread0.079 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicScientific Computing and Data ManagementFrench-language works237,207