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Record W6981814241

Feature Story: Safer drinking water and better crop yields

2015· other· en· W6981814241 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueoURspace (University of Regina) · 2015
Typeother
Languageen
FieldComputer Science
TopicArtificial Intelligence Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSAFERAgricultureFood securityCropQuality (philosophy)Sustainable agriculture
DOInot available

Abstract

fetched live from OpenAlex

Increasing crop yields, developing new antimicrobial therapies, and ensuring safe drinking water are among the topics that will be discussed at a microbiology conference at the University of Regina from June 15 to 18, 2015. Research leaders in the discipline from across Canada and other countries will be presenting their results at the University of Regina for the 65th annual Canadian Society of Microbiologists conference. “The scientific conference addresses many aspects of microbiology, and the topics highlight the importance of microbiology in society. For example, specific sessions will focus on how microbes can be used to increase crop yields and help improve food security while helping to promote sustainable agricultural practices. There is also a focus on water quality and impacts to water quality,” says Dr. Chris Yost, professor in the department of biology at the University of Regina.

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.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.221
Teacher spread0.202 · 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