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Record W1982048456 · doi:10.4031/002533205787465823

MATE ROV Competitions Bring Ocean Science and Technology to Students and Educators across the U.S. and Canada

2005· article· en· W1982048456 on OpenAlexaboutno aff
Jill Zande, D. Michel, Deidre Sullivan

Bibliographic record

VenueMarine Technology Society Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsnot available
FundersDivision of Ocean SciencesNational Aeronautics and Space Administration
KeywordsRemotely operated underwater vehicleOcean scienceOceanographyFisheryEngineeringPolitical scienceGeologyComputer scienceBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

Despite our nation's increasing reliance on the ocean environment, students and educators are often unaware of ocean career opportunities and the knowledge and skills required to enter those careers (Sullivan et al., 2006; Sullivan, 2002). A consequence of this lack of awareness is a shortage of skilled individuals who can fill ocean workforce needs (MATE Forum, 1996; Zande & Sullivan, 2003). The Marine Advanced Technology Education (MATE) Center and the Marine Technology Society's (MTS) ROV Committee created the remotely operated vehicle (ROV) competition to address this issue and bring ocean science and technology to students and educators across the U.S. and Canada. Since 2001, the MATE Center and the ROV Committee have engaged thousands of students and educators from middle schools through universities in developing ROVs for tasks based on real workplace situations. In doing so, the program has promoted ocean issues and careers, connected students and educators with employers and working professionals, and helped students to develop valuable technical, problem solving, and teamwork skills.

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 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)0.000
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 score0.282
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

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

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 head0.003
GPT teacher head0.231
Teacher spread0.228 · 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 teacher head, not a consensus.

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

Citations1
Published2005
Admission routes1
Has abstractyes

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