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Record W1794002062 · doi:10.54782/jwm.v29i1.474

The New Alberta Hail Suppression Project

2021· article· en· W1794002062 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Journal of Weather Modification · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsAlberta Health
Fundersnot available
KeywordsStormMeteorologyCloud seedingSeedingWeather modificationWinter stormRadarEnvironmental scienceSevere weatherGeographyAeronauticsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

A new hail suppression project was started in Alberta in 1996. Weather Modification Inc. (WMI) of Fargo, North Dakota was awarded a five year contract by the Alberta Severe Weather Management Society of Calgary, Alberta to conduct cloud seeding to reduce urban property damage from hail, particularly for the Calgary and Red Deer areas. The operational program runs from June 15th to September15th. This project is rather unique because it is funded entirely by private insurance companies with the sole intent to mitigate the damage of property by hail storms. The seeding program is based upon the hailstorm conceptual model, seeding methods, and storm forecasting techniques of the previous long-term hail research project conducted by the Alberta Research Council from the late 1960’s through 1985. In 1996, a C-band weather radar with computer recording and communications systems and three cloud seeding aircraft were dedicated to the project. The aircraft and radar crews provided 24 hr coverage, seven days a week throughout the period. The program has been welcomed by the local communities and has rekindled much interest in cloud seeding.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.049
GPT teacher head0.270
Teacher spread0.221 · 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