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Record W4406421470 · doi:10.47456/rf.v20i31.47179

How we learn our names is written in the colour of the sky

2024· article· en· W4406421470 on OpenAlexaffabout
Michael B. MacDonald

Bibliographic record

VenueRevista Farol · 2024
Typearticle
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSkyPsychologyHistoryComputer scienceAstronomyPhysics

Abstract

fetched live from OpenAlex

The summer sky is now too often the colour of turmeric. Forest fire smoke, blown in from hundreds of kilometers away, gets stuck like too-wide nets between buildings. Edmonton, Alberta, where I have lived for nearly twenty years is a city on the edge of the Canadian Boreal Forest. It is the largest part of the Taiga, the second largest forest in the world that stretches across Canada and continues across Iceland, Norway, Sweden, Finland, Russia, Mongolia, and Japan. In Canada it covers 270 million hectares between flat prairie and the treeless arctic tundra. The Boreal Forest is the homeland of more than 600 Indigenous communities, most of the known fossil fuel reserves in Canada, it stores more than 208 billion tons of carbon (11% of the world’s total), and along with the Amazon Rainforest is the second of the two great lungs of the world.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.690
Threshold uncertainty score0.113

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.039
GPT teacher head0.303
Teacher spread0.265 · 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 designNot applicable
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
Published2024
Admission routes2
Has abstractyes

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