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Record W4388982064 · doi:10.3934/era.2023376

Optimal strategy for removal of greenhouse gas in the atmosphere to avert global climate crisis

2023· article· en· W4388982064 on OpenAlex
N. U. Ahmed, Saroj Biswas

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

VenueElectronic Research Archive · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsUniversity of Ottawa
FundersTemple University
KeywordsGreenhouse gasAtmosphere (unit)Optimal controlConvergence (economics)GreenhouseComputationControl (management)Environmental scienceNatural resource economicsMathematical optimizationSpace (punctuation)EconomicsEnvironmental economicsComputer scienceMathematicsMeteorologyGeographyAlgorithmMacroeconomics

Abstract

fetched live from OpenAlex

<abstract><p>In this paper, we propose a space-time dynamic model for describing the temporal evolution of greenhouse gas concentration in the atmosphere. We use this dynamic model to develop an optimal control strategy for reduction of atmospheric pollutants. We prove the existence of optimal policies subject to control constraints. Further, we present necessary conditions of optimality using which one can determine such policies. A convergence theorem for computation of the optimal policies is also presented. Simulation results illustrate removal of greenhouse gas using the optimal policies.</p></abstract>

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)0.002
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 score0.110
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.329
Teacher spread0.308 · 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