MétaCan
Menu
Back to cohort
Record W2587179542

Centre of Population of Saskatchewan, Canada

2017· article· en· W2587179542 on OpenAlexaboutno aff
Arzu Sardarli, Thuntida Ngamkham, Andrei Volodin

Bibliographic record

VenueThailand Statistician Thailand · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyPopulationSpatial variabilityDemographyPhysical geographyStatisticsMathematicsSociology
DOInot available

Abstract

fetched live from OpenAlex

For many years the temperature and its temporal and spatial dynamics have been one of the determinants of demographic processes. Some scholars classify the temperature as of the environmental factors correlating with births seasonality in human population.  Usually researchers use the value of temperature averaged over a territory (country, state or province). Perhaps for cases, where the territory is relatively small size of, or the temperature is homogenous over the territory, the spatial averaging of the temperature can be found reasonable. In case of Canada and Canadian provinces, where the temperature difference between the south and north is much greater than in the most of other countries, the spatial averaging of temperature over the large areas leads to significant increase of the uncertainty of measurement. The use of temperature values measured at the centre of population could significantly increase the accuracy of birth vs. temperature correlation analysis. Within the reported studies we have determined the center of population of the province of Saskatchewan of Canada.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.505

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 studies0.0010.000
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 head0.013
GPT teacher head0.272
Teacher spread0.259 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThailand Statistician ThailandSame topicMigration, Aging, and Tourism StudiesFrench-language works237,207