MétaCan
Menu
Back to cohort
Record W7041264943

Half life

2006· dissertation· en· W7041264943 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2006
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAtomic energyInscribed figureUranium oreUraniumNexus (standard)Uranium minePort (circuit theory)Uranium mining
DOInot available

Abstract

fetched live from OpenAlex

"Half Life," a novel in progress, includes three main settings and storylines: a fictional family's experience in Ukraine at the time of the Chernobyl disaster; Ontario's Ottawa Valley and the author's family from 1976 to 1989; and the life of Gilbert Labine, a prospector and mining executive in Canada during the first half of the twentieth century. "Half Life" posits uranium as the nexus between these apparently discrete fields: Labine discovered the mineral on Canadian soil in 1929 and built a refinery at Port Hope that continues to have political, environmental and health consequences. Canadian uranium went into the first atom bombs and, along with the domestic nuclear industry that grew, in part, out of Labine's discovery, remains one of the country's major exports. Radioactive waste from Port Hope, the nearby Chalk River facility of Atomic Energy and Control Limited, and high concentrations of naturally occurring uranium inscribed and effected the life of the author's family in the Ottawa Valley, where, coincidentally, Labine himself was also raised. And in Chernobyl, as well as in Hiroshima and Nagasaki (which will form yet another field in the finished novel), is found the most public and dramatic expression of uranium's divided nature as both promise and threat. "Half Life" takes this duality as both thematic and formal inspiration and uses the idea of mutation, perhaps uranium's most frightening potential, to connect characters, places and events and to put pressure on the division between fiction and non-fiction.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.258
Teacher spread0.231 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueSpectrum Research Repository (Concordia University)Same topicForensic Entomology and Diptera StudiesFrench-language works237,207