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Predicting long-term climate changes in Iraq

2021· article· en· W3208636558 on OpenAlexaff
Mohammed Abdaki, Ahmed R. Al-Iraqi, Raid Mahmood Faisal

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDownscalingRepresentative Concentration PathwaysEnvironmental scienceAir temperatureClimatologyClimate changeSurface air temperatureBaseline (sea)Coupled model intercomparison projectClimate modelMean radiant temperatureMeteorologyAtmospheric sciencesGeographyGeology

Abstract

fetched live from OpenAlex

Abstract Changes in air temperature have a significant impact in Iraq due to global climate change. The objective of this study is to project future trends of air temperature in Iraq. In this study, the future air temperature was projected for 2025, 2050, 2075 and 2100 from the CCSM4 climate model belong to CMIP5 under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenario for Iraq. The historical observed air temperature data (1950 – 2014) acted as referenced as the mean air temperature data obtained from 18 meteorological stations. Statistical downscaling has implemented. The model outputs were calibrated by using around 80% of the observed historical and model historical data. After that, it proved a significant performance of a statistical downscaling process for simulation air temperature for future periods. The results revealed that the mean air temperature would increase under the four RCPs scenarios with different levels. The lower increase rate belongs to the RCP2.6 scenario, the increase rate is expected to be (0.5-0.8 °C) above the observed historical level. However, the RCP8.5 has the highest rate at (4.1 -6 °C) while, the RCP4.5 and RCP6.5 have (1-2 °C) and (2-4 °C) respectively. On the other hand, the temperature expands direction is from the south toward central, west and north of Iraq.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.998

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 studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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.

Study designObservational
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

Citations10
Published2021
Admission routes1
Has abstractyes

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