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Modeling the Dynamics of Continental Shelf Carbon

2010· review· en· W2110467951 on OpenAlexaff
Eileen E. Hofmann, Bronwyn Cahill, Katja Fennel, Marjorie A. M. Friedrichs, Kimberly Hyde, Cindy Lee, Antonio Mannino, Raymond G. Najjar, John E. O’Reilly, John Wilkin, Jianhong Xue

Bibliographic record

VenueAnnual Review of Marine Science · 2010
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsDalhousie University
FundersNational Oceanic and Atmospheric AdministrationNational Aeronautics and Space Administration
KeywordsContinental shelfContext (archaeology)Biogeochemical cycleContinental marginForcing (mathematics)Carbon cycleEnvironmental scienceClimatologyOceanographyGeologyEcologyEcosystem

Abstract

fetched live from OpenAlex

Continental margin systems are important contributors to global nutrient and carbon budgets. Effort is needed to quantify this contribution and how it will be modified under changing patterns of climate and land use. Coupled models will be used to provide projections of future states of continental margin systems. Thus, it is appropriate to consider the limitations that impede the development of realistic models. Here, we provide an overview of the current state of modeling carbon cycling on continental margins as well as the processes and issues that provide the next challenges to such models. Our overview is done within the context of a coupled circulation-biogeochemical model developed for the northeastern North American continental shelf region. Particular choices of forcing and initial fields and process parameterizations are used to illustrate the consequences for simulated distributions, as revealed by comparisons to observations using quantitative statistical metrics.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.263
Teacher spread0.249 · 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 designOther design
Domainnot available
GenreReview

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

Citations102
Published2010
Admission routes1
Has abstractyes

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