MétaCan
Menu
Back to cohort
Record W3135896681

Nelvana of the Northern Lights

2014· book· en· W3135896681 on OpenAlexaboutno aff
H M W Nicholson, Rachel N. Richey, Benjamin Woo, Michael Hirsch

Bibliographic record

VenueE-Artexte (Artexte) · 2014
Typebook
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsWonderMammothComicsShot (pellet)Art historyHistoryArtGeographyArchaeologyPolitical scienceLawPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

"Nelvana of the Northern Lights returns from the lost pages of Adrian Dingle’s Triumph Comics! Nelvana was one of the world’s very first super-heroines, predating Wonder Woman by several months, and is among the ranks of the first Canadian superheroes to emerge after Canada placed an embargo on US luxury goods during WWII. First appearing in 1941, Nelvana was tasked with protecting Canada’s northern lands. Using the powers of the northern lights, Nelvana could fly at incredibly fast speeds, become invisible, and even turn into dry ice! She used her great powers to ward off Nazi invaders, shady fur traders, subterranean mammoth men, and inter-dimensional ether people." -- publisher's website.

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.083
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0060.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0830.021

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 head0.012
GPT teacher head0.182
Teacher spread0.170 · 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
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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueE-Artexte (Artexte)Same topicComics and Graphic NarrativesFrench-language works237,207