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Record W2745873091 · doi:10.13034/jsst.v10i1.126

Importing Data from Shapefiles and Pathfinding along Generated Nodes

2017· article· en· W2745873091 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Student Science and Technology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsShapefilePathfindingComputer scienceCartographyNode (physics)GeographyShortest path problemWorld Wide WebGraphTheoretical computer scienceMetadataEngineering

Abstract

fetched live from OpenAlex

The Shapefile format is a particular standard for storing GIS (Geographic Information System) data, designed and developed by the Environmental Systems Research Institute (ESRI). The purpose of this project was to extract the binary data describing the City of Lethbridge from ESRI Shapefiles, and then to demonstrate an ability to utilize and modify this data. The utilization component centered on pathfinding and visually drawing the data, while the modification component involved the creation of a new, human-readable file type which contained the processed Shapefile data. These goals were accomplished by converting the Shapefile data into custom ‘Node’ objects in C++ code. These nodes form the basis for further development, as more attributes can easily be added to them as needed. The implemented pathfinding is a matter of picking a starting and ending node, and travelling across their adjacent nodes until a shortest path is found, a search algorithm called A* (read: A Star). Although further work is necessary for a robust product, this platform is already highly modular and is freely available open source. Le format Shapefile est un standard particulier pour le stockage des données du système d’information géographique (SIG), conçu et développé par l’Institut de Recherche des Systèmes Environnementaux (ESRI). Le but de ce project était d’extraire les données binaires qui décrivent la ville the Lethbridge des Shapefiles ESRI, et de démontrer que ces données peuvent être utilisées et modifiées. Le composant d’utilisation était centré sur la navigation et la visualization des données, tandis que le composant de modification a demandé la création d’un nouveau format lisible aux humains qui contient les données Shapefile traitées. Ces buts ont été accomplies en convertissant l’information Shapefile en objets ‘nœud’ personnalisés dans le langage de programmation C++. Ces nœuds forment la base pour les développements plus approfondis, car plus d’attributs peuvent être facilement ajoutés aux nœuds lorsque nécessaire. Le système de navigation implémentée est alors une question de choisir un nœud de départ et de terminaison, puis voyager à travers leurs nœuds adjacents jusqu’à la découverte de la route la plus courte. Ce procès informatique est l’algorithme de recherche A* (lu : A Star). Quoi qu’encore plus de travail soient nécessaire pour le développement d’un produit able, cette plateforme est déjà très modulaire et disponible à l’open-source.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.084
GPT teacher head0.391
Teacher spread0.307 · 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