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Record W7130808701

Nest of Stone

2014· other· en· W7130808701 on OpenAlexaboutno aff
Kim Noce

Bibliographic record

VenueUniversity of the Arts London Research Online (University of the Arts London) · 2014
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMovie theaterGermanAtmosphere (unit)George (robot)Jury
DOInot available

Abstract

fetched live from OpenAlex

Nest of stone is a short animated film reflecting on death as a personal monument. “The bleak and frosty atmosphere of a late autumnal day in a graveyard are pixelated in this narrated tale of recollection. The narrator weaves a sad tale as the inscriptions on the tomb stones trigger memories that she finds uncomfortable, as she gains acceptance of the nature of death and its place in life is pondered over and assessed reaching a satisfying conclusion.” (Steve Henderson in Skwigly Online magazine Animation) Awards 2015 Be There!, JURY PRIZE SHORT FILM Corfu, Greece Official selection World Premiere 2014 68th Edinburgh International Film Festival, Uk(BAFTA Qualifying Festival) 2014 20th Encounters Film Festival, Bristol, UK (BAFTA Qualifying Festival & OSCAR Qualifying Festival) 2014 Aesthetica Short Film Festival, York, Uk (BAFTA Qualifying Festival) 2014 33th Vancouver International Film Festival, Vancouver, Canada 2014 Cinetekton Mexico 2014 Animated Dreams, Tallin, Estonia 2014 8th British Shorts Film Festival in Berlin, Germany 2015 Cinema Perpetuum Mobile International Short Film Festival, Minsk, Belarus 2015 w-o-l-k-e , Brussels, Belgium 2015 Biennale Women’s International Short Film Festival, Losone, Switzerland 2015 BioBioCine, Concepción International Film Festival, Chile 2015 Athens Animfest, Greece 2015 Be There!, Corfu, Greece Special Screening & Retrospectives 2015 Rising Tides, London, UK

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.435
Threshold uncertainty score0.807

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.001
Scholarly communication0.0070.004
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.4350.117

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 head0.039
GPT teacher head0.278
Teacher spread0.239 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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Same venueUniversity of the Arts London Research Online (University of the Arts London)French-language works237,207