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Urban Environmental Applications of GIScience

2011· book-chapter· en· W4250602491 on OpenAlexaboutno aff
Buket Ayşegül Özbakır

Bibliographic record

VenueAdvances in environmental engineering and green technologies book series · 2011
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
Fundersnot available
KeywordsGeographic information systemGIS and public healthGeographyContext (archaeology)Data scienceAcronymSpatial contextual awarenessSpatial analysisComputer scienceInformation systemCartographyRemote sensingEngineering

Abstract

fetched live from OpenAlex

Geographic Information Systems (GIS) are computer-based systems used to store and manipulate geographical data, and perform spatial analysis. These systems serve to reveal the patterns, relationships, and anomalies, or sometimes invisible characteristics of the geographical data in various applications. While the term “GIS” indicates an object or tool, GIScience, the acronym for Geographic Information Science, covers a broader context of methodologies behind spatial data analysis. Among different application areas of GIS, “environmental monitoring and modeling” plays a significant role in the development of the very first GIS in the world-The Canada Geographic Information Systems (CGIS) in the mid-1960s. After almost 40 years of history, significant changes and challenges took place in the geographic information research agenda. This chapter will point out some of the vital tools and methods used in GIScience (including GIS, remote sensing and 3D modeling) to grasp issues of our urban environments. With recent technological advances that facilitate our understanding of the environment; it is more evident that the vision of more “livable” cities is not too far but not easy as well.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.157
Teacher spread0.153 · 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 designNot applicable
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
Published2011
Admission routes1
Has abstractyes

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