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Record W2100810285 · doi:10.1260/0958-305x.25.8.1439

Calculation of Atmospheric Radiative Forcing (Warming Effect) of Carbon Dioxide at Any Concentration

2014· article· en· W2100810285 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.

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

Bibliographic record

VenueEnergy & Environment · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMcGill UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsRadiative forcingWater vaporAtmospheric sciencesCarbon dioxideGreenhouse gasRelative humidityEnvironmental scienceAtmosphere (unit)HumidityRadiative transferForcing (mathematics)Absorption (acoustics)Global warmingMeteorologyChemistryClimate changePhysicsAerosolOptics

Abstract

fetched live from OpenAlex

The Beer-Lambert law does not apply strictly to the relationship between radiative forcing (RF) of CO 2 and concentration in the atmosphere, i.e., ΔRF = 5.35ln(C/C o ). It is an approximation because water vapour competes unevenly with CO 2 over the IR absorption wavelength range. We propose a quadratic model as an improved approximation. It links concentration to RF thereby allowing RF calculation at any concentration, not just ΔRF. For example, at 378 ppmv of CO 2 , the level in 2005, it calculates RF = 8.67 W m −2 , or approximately 2.7% of the total RF of all the greenhouse gases. A second and independent method based on worldwide hourly measurements of atmospheric temperature and relative humidity confirms this percentage. Each method shows that, on average, water vapour contributes approximately 96% of current greenhouse gas warming. Thus, the factors controlling the amount of water vapour in the air also control the earth's temperature.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.915

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)0.0000.000

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 head0.171
Teacher spread0.168 · 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