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Record W3110691453 · doi:10.15353/acmla.n165.1886

Drones in Libraries: The Development of an Interdisciplinary Research Service Using Drones and 3D Modeling Technologies at Ryerson University Library

2020· article· en· W3110691453 on OpenAlexaffvenueabout
Dan Jakubek, Jimmy Tran

Bibliographic record

VenueBulletin - Association of Canadian Map Libraries and Archives (ACMLA) · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDroneService (business)Resilience (materials science)AeronauticsCertificationService modelEngineeringEngineering managementCoronavirus disease 2019 (COVID-19)Library scienceComputer sciencePolitical scienceBusinessMedicine

Abstract

fetched live from OpenAlex

On June 1, 2019, new rules for flying a Remotely Piloted Aircraft System (RPAS) or “drone” in Canada came into effect, requiring drone pilot certification to operate any drone between 250 g and 25 kg. In response to new regulations and the needs of our researchers, the Ryerson Library has initiated the development of a research service dedicated to supporting the use of drones and 3D modeling technologies. Before cancellation due to the Covid-19 pandemic, the joint CAG/CCA/CARTO-ACMLA conference - CAG 2020: Resilience on a Dynamic Planet - provided a national venue to showcase our progress to date. This report will summarize our workshop content and outline existing collaborations and future directions for our research and service.

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)0.007
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.383

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0110.003
Scholarly communication0.0120.005
Open science0.0020.014
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0290.008

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 head0.047
GPT teacher head0.222
Teacher spread0.175 · 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
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
Published2020
Admission routes3
Has abstractyes

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