MétaCan
Menu
Back to cohort
Record W7098634964

ADVANCING THE INTEROPERABILITY OF OCEAN SENSORS. WORKSHOP DEMONSTRATION AT OCEAN INNOVATION 2008

2016· article· en· W7098634964 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicWood and Agarwood Research
Canadian institutionsnot available
Fundersnot available
KeywordsInteroperabilityGeospatial analysisSensor webWireless sensor networkData accessArchitecture
DOInot available

Abstract

fetched live from OpenAlex

Abstract- The aim of this project is to implement a smart sensor worldwide net-work based on IEEE 1451 family of standards which can provide multiple interop-erable clients to access geospatial and sensor data for national local monitoring. This paper presents part of an integrated ocean observing system (IOOS)developed by SARTI research group which offers remotely access via the Web to sensors and sensor networks in Canada, USA and Europe. It also describes the capabilities and functions of such a network and possibilities to access multiple sensor observation and sensors via OGC SWE and IEEE 1451. The architecture of sensor network is based on the IEEE 1451.0. I.

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)0.011
metaresearch head score (Gemma)0.004
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: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.020
GPT teacher head0.275
Teacher spread0.256 · 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
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicWood and Agarwood ResearchFrench-language works237,207