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Record W6989883119

Characteristics and formation of precipitation over the Kananaskis Emergency Site during March and April 2015

2016· dissertation· en· W6989883119 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationSnowSnowflakeStormRain and snow mixedFreezing rain
DOInot available

Abstract

fetched live from OpenAlex

A field campaign was conducted in March and April 2015 in the Kananaskis Valley of Alberta to investigate the formation and characteristics of ice crystals and solid precipitation particles in the lee of the Rocky Mountains. This thesis examines data from 11 storms producing mainly light precipitation within generally sub-saturated surface and near-surface conditions. Instruments utilized include soundings, radar images, and surface observations, but the focus is on the analysis of 1,183 microphotographic images of precipitation particles. The particles (diameters up to 24 mm) were placed into 12 categories with rimed irregular snow particles being most common. Unrimed and rimed particles were commonly (14% of images) observed simultaneously and 62% of particles were rimed. Rimed, dense particles were less likely to sublimate before reaching the surface in the dry sub-cloud region that was at least partially a result of the air aloft being directed mainly towards the east and ‘downslope’.

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 score0.502
Threshold uncertainty score0.991

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.014
GPT teacher head0.205
Teacher spread0.190 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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