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Record W2096217132 · doi:10.1145/2556288.2557304

LACES

2014· preprint· en· W2096217132 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

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicVideo Analysis and Summarization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVideo productionComputer scienceWorkflowVideo editingCasualMultimediaVideo captureStatus quoNon-linear editing systemProcess (computing)CLIPSOverhead (engineering)Production (economics)Video processingComputer graphics (images)Smacker videoArtificial intelligenceDatabase

Abstract

fetched live from OpenAlex

Video authoring activity typically consists of three phases: planning (pre-production), capture (production) and processing (post-production). The status quo is that these phases occur separately, and the latter two have a significant amount of "slack time", where the camera operator is watching the scene unfold during capture, and the editor is re-watching and navigating through recorded footage during post-production. While this process is well suited to creating polished or professional video, video clips produced by casual video makers as seen in online forums could benefit from some editing without the overhead of current authoring tools. We introduce LACES, a tablet-based system enabling simple video manipulations in the midst of filming. Seamless in-situ integration of video capture and manipulation forms a novel workflow, allowing greater spontaneity and exploration of video creation.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.284

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 science0.0010.001
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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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

Quick stats

Citations14
Published2014
Admission routes1
Has abstractyes

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