MétaCan
Menu
Back to cohort

MOESM1 of Projected local rain events due to climate change and the impacts on waterborne diseases in Vancouver, British Columbia, Canada

2019· article· en· W6957862094 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2019
Typearticle
Languageen
FieldComputer Science
TopicHandwritten Text Recognition Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationClimate changeTurbidityWaterborne diseasesWater supplyAir temperatureHydrology (agriculture)

Abstract

fetched live from OpenAlex

Additional file 1: Figure S1. Map depicting the watersheds and supply areas for the Metro Vancouver Drinking Water System (Source: City of Surrey, BC, Canada, 2015). Figure S2. Comparison of weekly cumulative precipitation observed at Environment Canada weather station 698 and Pacific Climate Impacts Consortium (PCIC) interpolated precipitation for the grid containing the weather station (1997–2009). Figure S3. Time series of weekly cases of cryptosporidiosis and giardiasis, weekly cumulative precipitation, preceding dry days and average weekly turbidity from 1997 to 2009.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.138
Threshold uncertainty score0.461

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.009
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1380.022

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 head0.014
GPT teacher head0.219
Teacher spread0.205 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicHandwritten Text Recognition TechniquesFrench-language works237,207