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Record W1655754084

Radiative and Cloud Microphysical Effects of Forest Fire Smoke over North America and Siberia

2007· dissertation· en· W1655754084 on OpenAlex
Brian Vant‐Hull

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDigital Repository at the University of Maryland (University of Maryland College Park) · 2007
Typedissertation
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsRadiative transferSmokeEnvironmental scienceCloud computingGeographyMeteorologyAtmospheric sciencesClimatologyGeologyPhysicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Aerosol affects climate both through direct radiative effects and by indirect effects on cloud development. Absorbing aerosols have additional influence on the vertical temperature profile of the atmospheric column. Radiative effects of smoke are studied for the case of a Canadian smoke plume that blanketed the U.S. mid-Atlantic seaboard. Optical properties derived from aircraft in situ measurements demonstrate that the smoke formed a layer with a base 2 km above the surface, and absorptive heating in this layer could have strengthened and maintained the subsidence inversion responsible for the layer structure. An optical model of the smoke formed from a blend of aircraft and AERONET measurements allows retrieval of the smoke aerosol by satellite, so that comparisons are possible to measurements made by any instrument in the region. For this case an optical model based purely on AERONET measurements provides the best satellite retrieval of optical depth, but a model based mainly on aircraft measurements agreed best with spectrum wide-forcing measurements, demonstrating the dangers of a simple optical model for all retrievals. A study done in the Amazonian burning season demonstrates that sun/observation geometry is useful to control bias from shadowed and illuminated portions of clouds. Sub-pixel mixing of cloud and aerosol also produces bias that is minimized for optically thick clouds. Since such biases can never be fully eliminated, the only valid study is a comparison of two data sets with equivalent geometry and so, presumably, equal bias. Canada and Siberia were chosen so that forested areas are compared at the same latitudes. Summertime Canadian aerosol is primarily smoke, while Europe contributes a great deal of sulfate to Siberia aerosol. The average cloud droplet size was significantly smaller in Siberia, as expected from the higher sulfate load with greater activity as cloud condensation nuclei (CCN). The aerosol indirect effect on cloud microphysics increases with aerosol loading in both regions, but much more so in Canada. This is attributed to a large sulfate background in Siberia, so the addition of smoke makes a smaller percentage change to the amount of cloud CCN.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.002
GPT teacher head0.153
Teacher spread0.151 · 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