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Record W2098250970 · doi:10.5539/jgg.v2n1p83

Climatic Variability and Linear Trend Models for the Five Major Cities of Pakistan

2010· article· en· W2098250970 on OpenAlexvenueno aff
Naeem Sadiq, Muhammad Shahid Qureshi

Bibliographic record

VenueJournal of Geography and Geology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsMean radiant temperaturePrecipitationMaximum temperatureClimatologyMonsoonEnvironmental scienceGeographyAtmospheric sciencesClimate changeMeteorologyGeology

Abstract

fetched live from OpenAlex

Estimates show that the average temperature of Earth’s near surface air and oceans has raised by 0.74 ± 0.180C during last 100 years. In this work the impact of the same has been explored for major urban areas of Pakistan. For this exploration long-term mean, mean-maximum and mean-minimum temperatures for the period 1961 to 2007 have been studied. The precipitation in the major cities of Pakistan, are also studied. The maximum increase in mean temperature is found to be 0.0570C per year (in Quetta). The minimum increase is found to be 0.0190C per year (in Peshawar). Both these increments are more than the global mean. An isolated discrepancy is found in mean maximum temperature of Lahore but the same can be explained in terms of heavy and prolonged monsoon rains. Moreover, precipitation in Karachi is found to be decreasing that needs to be further explored.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.888
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2010
Admission routes1
Has abstractyes

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