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Record W6931396188 · doi:10.5281/zenodo.6578776

Our Ocean, Our Future - Ocean Sciences Meeting

2022· article· en· W6931396188 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsEsri (Canada)
Fundersnot available
KeywordsOverpopulationClimate changeFocus (optics)Earth system scienceClimate scienceNatural (archaeology)VisualizationInformation revolution

Abstract

fetched live from OpenAlex

Slides from the Ocean Sciences Meeting 2022 Keynote Address. As was discussed throughout the conference, the FLUIDITY of our ocean planet is our FUTURE. It is essentially what gives it life. And yet, as we know, our world today is simply, IN TROUBLE. It's safe to say that what we've done with environmental degradation and the issues of social instability are creating a significant amount of uncertainty for all of us, and then there is the unthinkable situation in Ukraine and with the associated crisis in the Black Sea. These issues are overwhelming. And somehow they're all interconnected — climate change, natural disasters, loss of nature in the ocean, overpopulation along the ocean. Addressing these issues with equitable SOLUTIONS is going to be challenging for all of us for the rest of our lives, requiring new governance, new policies, new market approaches, and certainly new technologies which is a focus of the talk. ONE MAJOR SOLUTION I would argue is GEOGRAPHIC, where a so-called Geographic Approach can come to the rescue to help us organize and optimize our knowledge of the ocean (ALL OF OUR KNOWLEDGE, FROM ALL CULTURES, and GEOGRAPHIES) to solve these challenges and to SAVE it. And what I mean by a Geographic Approach is really a Way of Thinking and Problem Solving … … that Integrates spatial data Science & Information Into How We Explore, Designate, Understand, Manage and Communicate about the ocean This approach integrates and supports powerful methodologies: geoanalytics, creating insights and understanding; geovisualization, a language through maps and visualization for communicating the content and the context of our world; geodesign, designing sustainable and inclusive futures; geocollaboration, engaging all the community; and geoaccounting, being able to account for all the factors, setting up balanced measures that are driven by our shared value of the ocean, our knowledge, climate financing and much more. https://www.aslo.org/osm2022/plenary-speakers/

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.443
Threshold uncertainty score0.795

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.4430.279

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 head0.049
GPT teacher head0.286
Teacher spread0.237 · 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.

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
Published2022
Admission routes1
Has abstractyes

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