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Record W2808220468 · doi:10.7251/vetjen1801132m

ICT SYSTEMS FOR MONITORING AND PROTECTION OF WILDLIFE IN THEIR NATURAL ENVIRONMENT

2018· article· en· W2808220468 on OpenAlexaff
Branko Marković, Drago Nedić, Savo Minić

Bibliographic record

VenueВЕТЕРИНАРСКИ ЖУРНАЛ РЕПУБЛИКЕ СРПСКЕ · 2018
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWildlifeComputer scienceInternet of ThingsThe InternetPoint (geometry)Natural (archaeology)DroneEndangered speciesBusinessComputer securityWorld Wide WebEcologyGeography

Abstract

fetched live from OpenAlex

The paper deals with systems for monitoring and protection of wildanimals in their natural environments and the use IoT technologies and solutionsin protected nature reserves. The paper also examines the reasons and possibilitiesfor implementing the above mentioned technical solutions, especially in termsof protecting species from the red list of endangered species. In this sense, thepaper also discusses technological solutions and the possibilities of applying IoTworking framework, the concept of the Internet of animals, and the application ofthese technologies through various business and research models. Finally, the paperprovides examples of solutions from the point of view of the necessary infrastructure(servers, storage, internet, animal necklaces, stationary cameras and drones), aswell as from the point of view of data processing and the legal framework for theapplication of these solutions.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2018
Admission routes1
Has abstractyes

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