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Record W4392654709 · doi:10.5194/egusphere-egu24-17803

Why do sun-blocking drops high up in the air cool down the world less than we would expect?

2024· preprint· en· W4392654709 on OpenAlexaff
Moritz Günther, Hauke Schmidt, Claudia Timmreck, Matthew Toohey

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBlocking (statistics)Environmental scienceComputer scienceComputer network

Abstract

fetched live from OpenAlex

Some large rocks can burst out and put fire and smoke into the air, and I will call them fire rocks. Sometimes, the fire rocks also give off little drops in the higher part of the air, which stay there for a few years. Sun light has a hard time passing through these drops, and this makes the world a bit darker. When the fire rocks give off sun blocking drops, we would expect the world to get quite a lot colder, because the world now gets a lot less power from the sun. However, it actually gets only a bit colder: the change is less than we would expect. In order to find out why, I use a computer to study how the world would respond to the sun-blocking drops. The small cooling can be explained by the fact that some places of the world cool faster than others. One key place is that part of the world where the water is the warmest. On a paper that shows all the world's land and water, this place is in the left part of the largest water body. I will call it the warm water place. The drops that come from the fire rocks cool down the warm water place very much. When the warm water place cools down a lot, the rest of the world can remain more or less how it was before, because the warm water place has a strong control over the rest of the world. Over the warm water place there is a lot of up-going air, which means that it is well tied to the air that is closer to space. For this reason, the warm water place can even out the cooling very well, much better than any other place in the world. Making the world darker with these drops is a way to make the world colder, but we are most used to the case where the world gets warmer because humans put warming smoke into the air. We can put a number on the forces that are caused by the warming smoke and the cooling drops. In the end, warming and cooling are the same thing, just with a different sign, so we can look at them side by side. The forcing that comes from the warming smoke does not leave such a strong mark on the warm water place, and for this reason it is very good at making the world hotter. On the other hand, the warm water place responds in a strong way to the cooling drops that block the sun light, and this makes the cooling drops very bad at cooling the world.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.006

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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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