MétaCan
Menu
Back to cohort
Record W2074893592 · doi:10.1029/2009gl037946

Can cosmic rays affect cloud condensation nuclei by altering new particle formation rates?

2009· article· en· W2074893592 on OpenAlex
Jeffrey R. Pierce, P. J. Adams

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCosmic rayCloud condensation nucleiAerosolCloud coverAtmospheric sciencesPhysicsFlux (metallurgy)Particle (ecology)AstrophysicsEnvironmental scienceCloud computingMeteorologyGeologyChemistry

Abstract

fetched live from OpenAlex

Although controversial, many observations have suggested that low‐level cloud cover correlates with the cosmic ray flux. Because galactic cosmic rays have likely decreased in intensity over the last century, this hypothesis, if true, could partly explain 20th century warming, thereby upsetting the consensus view that greenhouse‐gas forcing has caused most of the warming. The “ion‐aerosol clear‐air” hypothesis suggests that increased cosmic rays cause increases in new‐particle formation, cloud condensation nuclei concentrations (CCN), and cloud cover. In this paper, we present the first calculations of the magnitude of the ion‐aerosol clear‐air mechanism using a general circulation model with online aerosol microphysics. In our simulations, changes in CCN from changes in cosmic rays during a solar cycle are two orders of magnitude too small to account for the observed changes in cloud properties; consequently, we conclude that the hypothesized effect is too small to play a significant role in current climate change.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score1.000

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)0.0000.001

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 head0.297
Teacher spread0.272 · 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