MétaCan
Menu
Back to cohort
Record W6936911905 · doi:10.58079/piph

Wer bin ich, und wenn ja, wie viele? Fallstudie (46): Soma

2020· article· de· W6936911905 on OpenAlexaboutno aff

Bibliographic record

VenueOpenEdition (OpenEdition) · 2020
Typearticle
Languagede
FieldArts and Humanities
TopicSports Science and Education
Canadian institutionsnot available
Fundersnot available
KeywordsSubject (documents)Context (archaeology)

Abstract

fetched live from OpenAlex

Von Arno Görgen Aus dem Meer kommt alles Leben, und auch der letzte Lebensatem der Menschheit wird im Meer ausgehaucht werden. Zumindest im Unterwasser-Horror von Soma. Als Spieler gerade noch todeskrank in Toronto, wache ich nach einem Hirn-Scan in der zerfallenen Unterwasserstation Pathos II auf. Ich lerne, dass nicht nur die gesamte Erdoberfläche und damit die Menschheit durch einen Meteoriteneinschlag ausgelöscht wurde, auch ich selbst bin nur noch ein in ein Mensch-Maschine-Wesen hoch...

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.093
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.003
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0470.010

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 head0.041
GPT teacher head0.260
Teacher spread0.219 · 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 designCase report
Domainnot available
GenreEmpirical

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueOpenEdition (OpenEdition)Same topicSports Science and EducationFrench-language works237,207