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Record W2518727865 · doi:10.54782/jwm.v48i1.551

Twenty Seasons of Airborne Hail Suppression In Alberta, Canada

2016· article· en· W2518727865 on OpenAlexaboutno aff
Daniel B. Gilbert, Bruce A. Boe, Terrence W. Krauss

Bibliographic record

VenueThe Journal of Weather Modification · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
Fundersnot available
KeywordsStormMetropolitan areaWinter stormGeographySevere weatherMeteorologyArchaeology

Abstract

fetched live from OpenAlex

After a catastrophic late-season hailstorm hit the Calgary, Alberta, metropolitan area in September 1991, causing about half a billion dollars (Canadian) in damage, the property and casualty insurance industry began actively seeking ways to actively mitigate hail damage. After nearly four years of intensive study and intra-industry negotiation, the Alberta Severe Weather Management Society (ASWMS) was born. The ASWMS was and is comprised of representatives of all the insurance companies making up >90% market share in southern Alberta, and through levies based on their market share, annually fund an airborne cloud seeding program having the exclusive purpose of reducing damaging hailfalls in metropolitan areas, the Alberta Hail Suppression Project (AHSP). Beginning in 1996, this program has become an annual endeavor conducted from June through mid-September. This paper summarizes the current structure and operations of the AHSP, and through radar data compares seeded and unseeded storms that occurred on 21 July 2015, one of the most active days of the 2015 storm season.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.226
Teacher spread0.201 · 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 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

Citations11
Published2016
Admission routes1
Has abstractyes

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