MétaCan
Menu
Back to cohort
Record W2809665189 · doi:10.29007/wvc2

Geomatics and Open Textbooks

2018· paratext· en· W2809665189 on OpenAlexaff
Scott Bell, Heather M. Ross, Jordan Epp, Kelsey M. Bates

Bibliographic record

VenueEasyChair preprint · 2018
Typeparatext
Languageen
FieldComputer Science
TopicMathematics, Computing, and Information Processing
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGeomaticsGeographyData scienceEngineeringCartographyLibrary scienceArchaeologyComputer science

Abstract

fetched live from OpenAlex

Geomatics is uniquely suited to the development, publication, and use of open textbooks. Here the concept of open textbooks will be introduced and the suitability of Geomatics will be examined. Geomatics (and GIS, GIScience, Cartography, Geodesy, etc.) have historic and archeological tendrils that stretch to some of the earliest artifacts related to human settlement. Furthermore, geomatics has been ever-present as humanity strove to improve, development, conquer, and acquire knowledge. This history and the dependence of modern geomatics on national interests means that the knowledge associated with teaching geomatics and related disciplines in primarily in the public domain and authors have relinquished restrictions related to copyright, intellectual property rules and laws, or other aspects of its use.

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)0.003
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.998
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.007
Science and technology studies0.0020.006
Scholarly communication0.0130.012
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0920.032

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 head0.030
GPT teacher head0.299
Teacher spread0.269 · 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.

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueEasyChair preprintSame topicMathematics, Computing, and Information ProcessingFrench-language works237,207