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Record W4393665617 · doi:10.5281/zenodo.10342524

Schooner 05: Poisson Mesh & Z-Brush Sculpting

2016· dataset· en· W4393665617 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2016
Typedataset
Languageen
FieldEngineering
TopicAssembly Line Balancing Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsBrushPoisson distributionComputer scienceComputer graphics (images)MathematicsMaterials scienceComposite materialStatistics

Abstract

fetched live from OpenAlex

This model is a combination of automated Poisson surface generation in Meshlab, and sculpting of the resultant model to account for blind spots in the original data and errors in the automated mesh generations. This schooner was found near Toronto's Fort York, unearthed along with the Queen's Wharf during a construction project. Scanning was done prior to removal for record keeping and reinforcement/rigging planning. The schooner is believe to date to the 18th-century. Check the data out here: https://skfb.ly/EMnT. A FARO Focus and Freestyle were used for the data capture. A news article on the removal of the schooner can be found here: http://www.cbc.ca/news/canada/toronto/toronto-schooner-recovered-from-construction-site-moved-to-fort-york-1.3099614 Source: Objaverse 1.0 / Sketchfab

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.019

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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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