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Record W2945192627 · doi:10.3389/fmars.2019.00309

The Importance of Connected Ocean Monitoring Knowledge Systems and Communities

2019· article· en· W2945192627 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueFrontiers in Marine Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsOcean Networks Canada SocietyUniversity of Victoria
FundersFisheries and Oceans CanadaCentre Scientifique de MonacoBelmont ForumPolar Knowledge Canada
KeywordsIndigenousTraditional knowledgeEnvironmental resource managementCitizen scienceResource (disambiguation)Ocean observationsKnowledge sharingData sharingBusinessKnowledge managementEnvironmental planningGeographyEcologyComputer scienceEnvironmental science

Abstract

fetched live from OpenAlex

Ocean monitoring will improve outcomes if ways of knowing and priorities from a range
\nof interest groups are successfully integrated. Coastal Indigenous communities hold
\nunique knowledge of the ocean gathered through many generations of inter-dependent
\nliving with marine ecosystems. Experiences and observations from living within that
\nsystem have generated ongoing local and traditional ecological knowledge (LEK and
\nTEK) and Indigenous knowledge (IK) upon which localized sustainable management
\nstrategies have been based. Consequently, a comprehensive approach to ocean
\nmonitoring should connect academic practices (“science”) and local community and
\nIndigenous practices, encompassing “TEK, LEK, and IK.” This paper recommends
\nresearch approaches and methods for connecting scientists, local communities, and
\nIK holders and their respective knowledge systems, and priorities, to help improve
\nmarine ecosystem management. Case studies from Canada and New Zealand (NZ)
\nhighlight the emerging recognition of IK systems in natural resource management, policy
\nand economic development. The in-depth case studies from Ocean Networks Canada
\n(ONC) and the new Moana Project, NZ highlight real-world experiences connecting
\nIK with scientific monitoring programs. Trial-tested recommendations for successful
\ncollaboration include practices for two-way knowledge sharing between scientists and
\ncommunities, co-development of funding proposals, project plans and educational
\nresources, mutually agreed installation of monitoring equipment, and ongoing sharing
\nof data and research results. We recommend that future ocean monitoring research
\nbe conducted using cross-cultural and/or transdisciplinary approaches. Vast oceans
\nand relatively limited monitoring data coupled with the urgency of a changing climate
\nemphasize the need for all eyes possible providing new data and insights. Community
\nmembers and ocean monitoring scientists in joint research teams are essential for
\nincreasing ocean information using diverse methods compared with previous scientific
\nresearch. Research partnerships can also ensure impactful outcomes through improved
\nunderstanding of community needs and priorities.

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 score0.030
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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