MétaCan
Menu
Back to cohort
Record W2014835144 · doi:10.1080/07055900.2011.557799

Continuous 1064/532 nm Lidar Measurements (CORALNet-UBC) in Vancouver, British Columbia: Selected Results from a Year of Operation

2011· article· en· W2014835144 on OpenAlexaffvenueabout
Ian G. McKendry, K. B. Strawbridge, A. R. Jones

Bibliographic record

VenueATMOSPHERE-OCEAN · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsEnvironment and Climate Change CanadaUniversity of British Columbia
Fundersnot available
KeywordsSnowMeteorologySoftware deploymentLidarEnvironmental scienceAir quality indexAerosolRange (aeronautics)Computer scienceRemote sensingGeographyEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

CORALNet-UBC was installed in April 2008 as a “proof of concept” and the first facility in a proposed cross-Canada network of similar lidars. Despite its location on the wet West Coast of Canada, data recovery ranged from a low of 45% of the total time in December, when heavy snowfall interrupted operations, to a high of 90% in July. The facility has been a spectacular success in terms of its operational characteristics (as measured by data recovery), the range of phenomena observed and the research that it has spawned. Examples are provided of the types of phenomena observed since its initial deployment. These include observations of boundary layer structure, cloud signatures, forest fire plumes and evidence of singular long-range pollutant and volcanic transport events. Observations from CORALNet-UBC complement data from other aerosol measurement initiatives in the region and provide a rich source of information that is relevant to researchers, operational forecasters and air quality managers. Future research will be directed at elucidating the processes and phenomena revealed by the instrument as well as developing new products that exploit the real-time forecasting and public advisory potential of the facility.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.016
GPT teacher head0.193
Teacher spread0.177 · 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.

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

Citations4
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueATMOSPHERE-OCEANSame topicAtmospheric aerosols and cloudsFrench-language works237,207