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Record W2782212435

Wi-Fi enabled CTDs in citizen science

2017· article· en· W2782212435 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOCEANS 2017 – Anchorage · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEnvironmental Monitoring and Data Management
Canadian institutionsOcean Networks Canada SocietyUniversity of Victoria
Fundersnot available
KeywordsCitizen scienceOcean observationsGeneral partnershipOcean scienceOceanographyQuality (philosophy)Marine conservationFisheries scienceGeographyEnvironmental resource managementFisheryEnvironmental scienceBusinessFishingGeologyFisheries management
DOInot available

Abstract

fetched live from OpenAlex

Monitoring the vast expanse of oceans presents a daunting challenge: how to take sufficient high-quality measurements with a relatively small group of specialized scientists and limited resources. Despite advances in remote sensing and autonomous technology, the oceans remain under-explored and under-sampled. One solution for addressing this challenge is to rely on citizen scientists, people outside of the scientific community, to collect oceanographic data. This paper describes how through three unique successful citizen science programs, oceanographers have increased temporal and spatial coverage of high-quality CTD profiles using a Wi-Fi enabled CTD and an easy to use mobile device app. The three programs include the Shelf Research Fleet: a group of fishermen partnered with Woods Hole Oceanographic Institute, the Pacific Salmon Foundation: a partnership between the Department of Fisheries and Oceans Canada and Ocean Networks Canada, and the Coastal Community Ocean Observers program: a network of citizen scientists distributed along remote Alaskan coasts.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.251
Teacher spread0.230 · 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