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Bayesian mixture modelling of species divergence

2004· article· en· W10167074 on OpenAlexaboutno aff
Mohd Bakri Adam, Kerrie Mengersen, Peter Baker

Bibliographic record

VenueHealthcare Management Forum · 2004
Typearticle
Languageen
FieldComputer Science
TopicBayesian Methods and Mixture Models
Canadian institutionsnot available
Fundersnot available
KeywordsBayesian probabilityDivergence (linguistics)EconometricsMathematicsComputer scienceStatisticsArtificial intelligenceEnvironmental science

Abstract

fetched live from OpenAlex

A defining--some would say peculiar--feature about Canada and Canadians is the strong position that we give social programs within our national identity. FORUM presents an essay by Dr. Thomas Noseworthy based on an address to the annual meeting of the Association of Canadian Medical Colleges in April 1996. In it, Dr. Noseworthy calls for a national health system. He sees the federal government retaining an important role in preserving medicare and, in fact, strengthening its powers in maintaining national consistency and standards. Dr. Noseworthy's views are contrary to the governmental decentralization and devolution of powers occurring across the country. In a "point/counterpoint" exchange on this issue, we have invited commentaries from three experts. Raisa Deber leads off by noting that while a national health system may be desirable, constitutional provisions would be an obstacle. Governments, says Deber, have an inherent conflict of interest between their responsibility for maintaining the health care system and their desire to shift costs. Michael Rachlis reminds us that medicare fulfills important economic as well as social objectives. It helps to support Canada's business competitiveness among other nations. The problem, say Rachlis, is that public financing of health care does not ensure an efficient delivery system. Michael Walker offers some reality orientation. He observes that Canada's health care system is based upon ten public insurance schemes with widely different attributes. While he supports a minimum standard of health care across the country, citizens should be able to purchase private medical insurance and have access to a parallel private health care delivery system. Ultimately, this debate is about who should control social programs: the provinces or the federal government? We'll let you, the readers, decide.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.436
Threshold uncertainty score0.729

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.266
Teacher spread0.233 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2004
Admission routes1
Has abstractyes

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