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Record W3183207942 · doi:10.3390/atmos12080955

Envisioning an Integrated Assessment System and Observation Network for the North Atlantic Ocean

2021· article· en· W3183207942 on OpenAlexaff
L.F. Coleman, Frank M. Mc Govern, Jurgita Ovadnevaitė, Darius Čeburnis, Thaize Baroni, Leonard A. Barrie, Colin O’Dowd

Bibliographic record

VenueAtmosphere · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsMcGill University
FundersNational Oceanic and Atmospheric Administration
KeywordsSustainabilityEarth system scienceBiosphereEnvironmental resource managementMultidisciplinary approachClimate changeEnvironmental scienceEnvironmental planningOceanographyEcologyGeologyPolitical science

Abstract

fetched live from OpenAlex

The atmosphere over the Atlantic Ocean is highly impacted by human activities on the surrounding four major continents. Globally, human activity creates significant burdens for the sustainability of key Earth systems, pressuring the planetary boundaries of environmental sustainability. Here, we propose a science-based integrated approach addressing linked science and policy challenges in the North Atlantic. There is a unique combination of ongoing anthropogenic changes occurring in the coupled atmosphere–ocean environment of the region related to climate, air and water quality, the biosphere and cryosphere. This is matched by a unique potential for the societies that surround the North Atlantic to systematically address these challenges in a dynamic and responsive manner. Three key linked science-policy challenges to be addressed as part of this proposed integrated regional approach are: (1) understanding physical and dynamic changes, (2) sustaining human and ecosystem health and (3) reducing existing knowledge gaps on the carbon budget and the Earth’s energy balance. We propose a North Atlantic multidisciplinary scientific assessment system and observation network to address these thematic challenges. We propose to build on and link with the existing research activities and observational networks and infrastructures to specifically address the key North Atlantic challenges that encompass a range of policy areas. This will strengthen the institutional response to weather, climate, environmental and ecological threats and reduce societal risk.

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 score0.438
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.209
Teacher spread0.194 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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