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Record W2061025621 · doi:10.1029/2007gl032165

Wildfire smoke injection heights: Two perspectives from space

2008· article· en· W2061025621 on OpenAlex

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

VenueGeophysical Research Letters · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsnot available
Fundersnot available
KeywordsSmokeEnvironmental scienceAerosolLidarTroposphereAtmospheric sciencesMeteorologyPlanetary boundary layerAtmosphere (unit)PlumeBoundary layerRemote sensingGeologyPhysics

Abstract

fetched live from OpenAlex

The elevation at which wildfire smoke is injected into the atmosphere has a strong influence on how the smoke is dispersed, and is a key input to aerosol transport models. Aerosol layer height is derived with great precision from space‐borne lidar, but horizontal sampling is very poor on a global basis. Aerosol height derived from space‐borne stereo imaging is limited to source plumes having discernable features. But coverage is vastly greater, and captures the cores of major fires, where buoyancy can be sufficient to lift smoke above the near‐surface boundary layer. Initial assessment of smoke injection from the Alaska‐Yukon region during summer 2004 finds at least about 10% of wildfire smoke plumes reached the free troposphere. Modeling of smoke environmental impacts can benefit from the combined strengths of the stereo and lidar observations.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.031
GPT teacher head0.296
Teacher spread0.265 · 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