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Record W4413059686 · doi:10.32006/eeep.2025.1.2229

MITIGATION OF ATMOSPHERIC GLOBAL WARMING BY HEAT TRANSFER

2025· article· en· W4413059686 on OpenAlexaff
Dimitre Karamanev

Bibliographic record

VenueEcological Engineering and Environment Protection · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsEnvironmental scienceGlobal warmingAtmosphere (unit)Carbon dioxide in Earth's atmosphereWork (physics)Carbon dioxideGreenhouse effectAtmospheric sciencesMeteorologyGreenhouse gasClimatologyClimate changeGeographyEngineeringGeologyChemistry

Abstract

fetched live from OpenAlex

Thus far, attempts to mitigate global warming have been based exclusively on reducing atmospheric carbon dioxide concentration. One problem with this approach is that the lifetime of CO2 in the atmosphere is very long, and the effect of reducing CO2 emissions on decreasing atmospheric temperature will only become significant after decades. In this work, I propose reducing or even halting the increase in global atmospheric temperature by removing sensible heat from the atmosphere and transferring it to other media, such as water and/or land mass. It is shown that the annual negative effect of heating ocean water will be close to non-existent. One of the main advantages is that it has an immediate effect on atmospheric temperature. The technology to realize this idea is simple, inexpensive, and relatively well-developed. It should be noted that the proposed solution to global warming is temporary and will only work for several decades. In the long term, reducing CO2 emissions should take precedence as the main method of mitigating global warming.

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 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 score0.454
Threshold uncertainty score0.545

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 head0.003
GPT teacher head0.160
Teacher spread0.158 · 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 teacher head, 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
Published2025
Admission routes1
Has abstractyes

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