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Record W7099164152

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

2005· article· en· W7099164152 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographic information systemHyperlinkResource (disambiguation)The InternetWeb applicationProcess (computing)User interfaceInterface (matter)
DOInot available

Abstract

fetched live from OpenAlex

Aboriginal rights are a key issue affecting resource development in Canada on Aboriginal lands. However, searching for land related information can be very time consuming and incomplete, particularly in remote Aboriginal areas. There is a need for an on-line system providing integrated information about land and its resources, such as natural resources, topography, existing infrastructures, statutory requirements and the regulations that might apply to various developments on the land. The goal of this research is to explore the role of new and emerging technologies, specifically GIS and Internet technologies, for resource management, and thereby develop a web GIS prototype to assist the resource development and management process involving Aboriginal communities. The research started by reviewing the use of web GIS by Aboriginal communities, governmental agencies and the private sector for aboriginal resource management. It then reviewed the current trends of web GIS technologies. This included a comparison of two commercial software ESRI ArcIMS and Intergraph Geomedia Webmap. ESRI ArcIMS was chosen to develop the prototype because of its easy maintenance and customization. In this research, the Yukon Territory, comprising 14 First Nations, was selected as the study area. Data for the Yukon First Nations was collected, edited, processed. A database was then designed and populated, and a web GIS prototype was developed. In the prototype implementation, an interactive users ’ interface was designed and developed. ArcIMS was customized to set up and enhance the website functions. Hyperlinks on the spatial layer were created, a mechanism for restricting access to the website was examined, and various advanced queries using ArcXML were developed and tested. Furthermore, a methodology, using combined vector and raster analysis, was developed to deal efficiently with multi-layer overlay analysis. iii

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)0.009
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.129
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.032
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0030.005
Scholarly communication0.0190.003
Open science0.0030.011
Research integrity0.0070.003
Insufficient payload (model declined to judge)0.8710.817

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 head0.040
GPT teacher head0.342
Teacher spread0.303 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2005
Admission routes1
Has abstractyes

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