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Record W2619256399 · doi:10.15273/pnsis.v49i1.6972

Nova Scotia science – accomplishments and challenges

2017· article· en· W2619256399 on OpenAlexvenueaboutno aff
Peter G. Wells

Bibliographic record

VenueProceedings of the Nova Scotian Institute of Science · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicResearch, Science, and Academia
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaNova (rocket)Political scienceLibrary scienceGeographyEngineeringComputer scienceAeronauticsArchaeology

Abstract

fetched live from OpenAlex

This PNSIS Issue and the ongoing series of monthly NSIS talks (2016-17) illustrate the range of science that is conducted in Nova Scotia and the region, and the amazing contributions that are celebrated continually across Canada and globally.The articles in this Issue cover topics that include the diversity of lichens and seaweeds found in Nova Scotia, the history of marine research, the importance of understanding changes in avian populations, advancements in management techniques in agriculture, insights into the process of discovery, and the requirement that scientific data be accessible to all potential users.Attention is drawn to two distinguished scientists, Arthur McDonald and Mary Ann White, noted for their special accomplishments and stellar careers.For the first time in recent issues of the Proceedings, reviews of two books prepared by local authors are included.The various articles are just a thin slice of the many annual research contributions from the scientific and engineering communities in the province.Of special note recently is the award of the NSERC 2017 Gerhard Herzberg Canada, Gold Medal for Science and Engineering, to Dalhousie Physics Professor Jeff Dahn for his leading work in battery technology -the science of lithium-ion batteries.This is the third recent Herzberg award to a researcher in Atlantic Canada.Previous winners were W. Ford Doolittle (2013) and Axel Becke (2015), also of Dalhousie University.This is surely a home run in Maritime science!Also noteworthy is the range of research conducted by young investigators, as shown by the contributions submitted each year to our student science writing competition, and the two excellent papers in this Issue by young investigators, Chen and Wilson, and by other young investigators in past years.This is so important -Nova Scotia must encourage and retain its highly educated young persons, as they will fuel the scientific breakthroughs, innovations, new products and a new and expanding economy for the region.Supporting all of this science requires political vision, leadership and investment for success.The Canadian federal government recognizes this fact, as well as the benefits of evidence-based/informed decision making, and to this

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.726

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0090.002
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0290.006

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 head0.258
GPT teacher head0.424
Teacher spread0.167 · 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.

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
Published2017
Admission routes2
Has abstractyes

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