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Record W2613400532 · doi:10.1021/cen-09422-scitech1

Firm footing for cloud seeding

2016· article· en· W2613400532 on OpenAlexaboutno aff
Janet Pelley

Bibliographic record

VenueC&EN Global Enterprise · 2016
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsSeedingCloud seedingCloud computingComputer scienceBusinessEngineeringAerospace engineeringOperating system

Abstract

fetched live from OpenAlex

The pilots at Weather Modification Inc. were standing by on alert on Aug. 12, 2012, when they got word from their staff meteorologist to jump in their planes and head toward a budding thunderstorm just west of Calgary, Alberta. Their mission: to prevent the formation of crop-destroying, car-denting hail by shooting flares loaded with silver iodide into cumulus clouds. Some of the pilots headed for the smooth, rain-free base of the clouds at 2,000 meters, where updrafts could pull the inorganic compound in. Other pilots flew to 5,500 meters, penetrating the tops of the billowy formations. Once in position, the aviators ejected the flares mounted on their planes. Theoretically, the silver iodide particles that spewed forth would catalyze supercooled water droplets in the clouds to freeze at a warmer temperature and more abundantly than they might have otherwise. The pilots hoped that this maneuver would redistribute the water vapor in

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.305

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)0.0000.000

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 head0.009
GPT teacher head0.242
Teacher spread0.234 · 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 designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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