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Record W6992816624

Meteorologist Leon Osborne explains what’s driving this year’s brutally cold conditions. Plus: tips to deal with it

2015· article· en· W6992816624 on OpenAlexaboutno aff

Bibliographic record

VenueUND Scholarly Commons (University of North Dakota) · 2015
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Naturally aspirated engineLimitingGloomHeading (navigation)
DOInot available

Abstract

fetched live from OpenAlex

Meteorologist Leon Osborne explains what’s driving this year’s brutally cold conditions. Plus: tips to deal with it The Red River Valley a.m. weather report says it all today: 15 to 20 below at night, with daytime highs this week bouncing along the zero mark. “This is January in North Dakota,” quipped Leon Osborne, Chester Fritz Distinguished Professor of Atmospheric Sciences, as he prepared for yet another road trip to talk weather to a regional farm group. “The first week of 2015 looks a lot like it did a year ago.” Bottom line: don’t stash the ear muffs and parkas just yet. If you’re new to Grand Forks this time of year, here’s some quick and basic winter survival tips that will serve you well when traveling the region ? until the spring thaw. Always have a working flashlight Make sure you maintain a full or near full gas tank When venturing long distances, always, let a friend or relative know your predicted arrival time Keep your cell phone charged when traveling. Pack blankets, extra high-energy foods, medicines and a supply of water Keep extra gasoline for emergency fuel in a safe place such as the trunk or box of your vehicle. If stranded, stay inside your vehicle Only run the motor for ten minutes each hour Crack the windows to avoid carbon monoxide poisoning Ensure the exhaust pipe of your vehicle is not blocked Tie a colored cloth to your antenna or door Raise the hood after the snow stops falling Exercise to keep warm and keep your blood flowing Osborne says, sure, it’s brutally cold, right now ? that’s normal in the northern Red River Valley. But there’s something else happening: there’s not nearly as much moisture in the storm systems that have tracked across the region this winter, he said. “That’s why we’re seeing black soil through the thin snow cover,” said Osborne, a pioneer in the development of the country’s 511 road weather information network. “The big factor in this drier pattern is a big, persistent ridge of high pressure across the eastern Pacific Ocean and across the western United States,” said Osborne. “The Jet Stream is curving over that ridge, and if you’re on the north or east side of it, it’s cold; if you’re on the south or west side of it, you’re warmer.” However, the Jet Stream doesn’t run like a race car, sticking to a consistent circular track. It’s a lot more like a big snake tracking across the sand. “On average this winter the Jet Stream is curving up over this ridge of high pressure, from southern Alaska, across the Yukon, down from a northwesterly direction across Saskatchewan and over the northern Plains,” Osborne said. “Sometimes we’re on the warm side, as we were in December. Then it shifts and we’re on the cold side, where were in November and are right now; thus, the bitterly cold temperatures.” And even though this year’s snowpack is a lot lighter than it was a year ago, there’s still a chance for plenty of snow this winter with deep frost possible where there is a lack of snow cover. However, going into spring, there’s plenty of subsoil moisture to get crops going this year. “The loose rule of thumb about weather is that it runs in 25 to 30 year cycles of wet to dry, and shorter term, four to six weeks from colder to warmer and back,” Osborne said. Let’s blame it on climate change, right? “Well that’s a lot more difficult to say,” Osborne noted. “We cannot say for certain, for example, how much any given weather phenomenon such as last week’s storm is affected by climate change,“ Osborne said. “For one, climate and weather are naturally variable. For another, basic physics tells us that you can’t live in an environment without impacting it, so it is expected that there is a climate change influence. But it’s difficult to tease out just exactly how much is anthropogenic climate change and how much is natural variability.” “We’re monitoring temperatures in central Canada,” Osborne said. “It’s minus 45 to 55 degrees Fahrenheit up there right now; plenty of cold air available ? and it has no place to go but south.” Juan Miguel Pedraza University & Public Affairs writer

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.178
Threshold uncertainty score0.597

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1780.143

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 head0.051
GPT teacher head0.279
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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