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Record W2604153005 · doi:10.23953/cloud.ijarsg.113

Urban Expansion and Loss of Agricultural Land: A Remote Sensing Based Study of Shirpur City, Maharashtra

2017· article· en· W2604153005 on OpenAlexaboutno aff
Y. J. Mahajan, Shrikant Mahajan, Bharat Patil, Sanjay Patil

Bibliographic record

VenueInternational Journal of Advanced Remote Sensing and GIS · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsHectareGeographyAgricultureAgricultural landLand usePopulationQuarter (Canadian coin)Agricultural economicsUrban expansionUrban areaGeographic information systemForestryCartographyEnvironmental protectionArchaeologyCivil engineeringDemographyEconomyEngineering

Abstract

fetched live from OpenAlex

In present urban expansion is important field of geographic study. This is an attempt to study the urban expansion and land use pattern of Shirpur city of Dhule district (MS). For that purpose used land landsat images (1991, 2001 and 2011) of Shirpur city. These satellite images further processed and analyzed by GIS software. Shirpur city lies in Shirpur tehsil and administrative head quarter of tehsil. Total population of Shirpur city was 44246 in 1991 that increase up to 76905 in 2011. The total area of Shirpur city is 1113.36 hectares out of that 459.64 hectares area under built up in 1991 that increase 760.16 hectares in 2011. This expansion of city area responsible for loss of agricultural land there were 540.33 hectares in 1991 that has been decline 298.72 hectares in 2011. Finely it is concluded that the growing population and its increasing demands of land for non-agricultural activities responsible for turn agricultural land for built up area, industrial plants, roads and plotting for future growth of city.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.218

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 head0.023
GPT teacher head0.273
Teacher spread0.250 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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