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Record W2169240492 · doi:10.2113/28.1.5

MEDAL OF SCIENTIFIC EXCELLENCE

2004· article· en· W2169240492 on OpenAlexaboutno aff

Bibliographic record

VenuePalynology · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsnot available
Fundersnot available
KeywordsMedalExcellenceEngineeringLibrary scienceEngineering ethicsPhilosophyHistoryComputer scienceArchaeologyEpistemology

Abstract

fetched live from OpenAlex

Barrie Dale, born on April 12, 1940, is a native of Sheffield, England. Following secondary school, he joined the staff of the University of Sheffield as a Research Technician in the Department of Geology. This appointment brought him into contact with the late Professor (then Dr.) Charles Downie, the late Professor Leslie R. Moore (Head of Department), Dr. Roger Neves and the various postgraduate students studying for their doctoral degrees in palynology. His duties included developing preparation techniques and type collections, teaching preparation methods to graduate students, and preparing palynological slides for examination by staff in the department together with assisting many of the postgraduate students including Graham Williams and David Wall. During this time he gained not only further technical qualifications but also an insight into the world of palynological research including dinoflagellates and their cysts. Years later, Charles Downie was to say that his greatest source of pride was in having taught palynology to Barrie Dale and David Wall. MEDAL OF SCIENTIFIC EXCELLENCE

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)0.025
metaresearch head score (Gemma)0.096
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: none
Teacher disagreement score0.110
Threshold uncertainty score0.369

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.096
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0040.002
Science and technology studies0.0090.008
Scholarly communication0.0240.006
Open science0.0040.019
Research integrity0.0140.019
Insufficient payload (model declined to judge)0.1100.108

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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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