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Record W2948543962 · doi:10.3389/fmars.2019.00277

Evolving and Sustaining Ocean Best Practices and Standards for the Next Decade

2019· article· en· W2948543962 on OpenAlexaff
Jay Pearlman, Mark Bushnell, Laurent Coppola, Johannes Karstensen, Pier Luigi Buttigieg, Francoise Pearlman, Pauline Simpson, Michèle Barbier, Frank Müller‐Karger, Cristian Muñoz-Mas, Peter Pissierssens, Cyndy Chandler, Juliet Hermes, Emma Heslop, Reyna Jenkyns, Eric P. Achterberg, Manuel Bensi, Henry C. Bittig, J. Blandin, Julie Bosch, Bernard Bourlès, Roberto Bozzano, Justin Buck, Eugene Burger, Daniel Serrano Cano, Vanessa Cardín, Miguel Charcos Llorens, A. Cianca, Hua Chen, Caroline Cusack, Eric Delory, René Garello, Gabriele Giovanetti, Valérie Harscoat, Susan E. Hartman, Robert Heitsenrether, Simon Jirka, Ana Lara-López, Nadine Lantéri, Adam Leadbetter, Giuseppe M.R. Manzella, Joan Masó, Andrea McCurdy, Eric Moussat, Manolis Ntoumas, Sara Pensieri, George Petihakis, Nadia Pinardi, Sylvie Pouliquen, Rachel Przeslawski, Nicholas Roden, Joe Silke, Mario N. Tamburri, Hairong Tang, Toste Tanhua, Maciej Telszewski, Pierre Testor, Julie Thomas, Christoph Waldmann, Frederick G. Whoriskey

Bibliographic record

VenueFrontiers in Marine Science · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsDalhousie UniversityOcean Tracking NetworkOcean Networks Canada Society
FundersNatural Environment Research CouncilSight Research UK
KeywordsBest practiceComputer scienceDocumentationSoftware deploymentData scienceWorld Wide WebSoftware engineering

Abstract

fetched live from OpenAlex

The oceans play a key role in global issues such as climate change, food security and human health. Given their vast dimensions and internal complexity, efficient monitoring and predicting of the planet’s ocean must be a collaborative effort of both regional and global scale. A first and foremost requirement for such collaborative ocean observing is the need to follow well-defined and reproducible methods across activities: from strategies for structuring observing systems, sensor deployment and usage, and the generation of data and information products, to ethical and governance aspects when executing ocean observing. To meet the urgent, planet-wide challenges we face, methods across all aspects of ocean observing must evolve into “Ocean Best Practices”. While many groups have created best practices, they are scattered across the Web or buried in local repositories and many have yet to be digitized. To reduce this fragmentation, we introduce a new open access, permanent, digital repository of best practices documentation (oceanbestpractices.org) that is part of the Ocean Best Practices System (OBPS). The new OBPS provides an opportunity space for the centralized and coordinated improvement of ocean observing methods. The OBPS repository employs user-friendly software to significantly improve discovery and access to methods. The software includes advanced semantic technologies for search capabilities to enhance repository operations. In addition to the repository, the OBPS also includes a peer reviewed Journal Research Topic, a forum for community discussion and a training activity for use of best practices. Together, these components serve to realize a core objective of the OBPS, which is to enable the ocean community to create superior methods for every activity in ocean observing from research to operations to applications that are agreed upon and broadly adopted across communities. Using selected ocean observing examples, we show how the OBPS supports this objective. This paper lays out a future vision of ocean best practices and how OBPS will contribute to improving ocean observing in the decade to come.

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)0.120
metaresearch head score (Gemma)0.191
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.120
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.191
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.012
Science and technology studies0.0040.008
Scholarly communication0.0280.031
Open science0.0080.017
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0070.008

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 head0.090
GPT teacher head0.397
Teacher spread0.307 · 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 designTheoretical or conceptual
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

Citations162
Published2019
Admission routes1
Has abstractyes

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