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Record W1977852848 · doi:10.4043/12116-ms

Reducing Greenhouse Gas By Ocean Nourishment

2000· article· en· W1977852848 on OpenAlexaboutno aff
I. S. Jones, Helen E. Young

Bibliographic record

VenueOffshore Technology Conference · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceGreenhouse gasCarbon sinkGreenhouse gas removalCarbon dioxide in Earth's atmosphereCarbon dioxideEffects of global warming on oceansAtmospheric carbon cycleNegative carbon dioxide emissionFossil fuelGlobal warmingCarbon sequestrationOceanographyClimate changeClimate change mitigationWaste managementEcologyGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract The burning of fossil fuels has produced vast amounts of carbon dioxide as a waste product. The United Nations Framework Convention on Climate Change requires industrialised countries to mitigate the rate of increase of atmospheric carbon dioxide. The most promising approach for mitigating climate change is to enhance biological sinks, the potentially largest of which is the ocean. Ocean nourishment is a technique for enhancing ocean sequestration by providing nutrients to regions of the ocean where they are in short supply. This process has the potential to generate tradeable carbon credits. It can be a very effective use for stranded offshore gas. Ocean nourishment can turn a waste product, carbon dioxide, into marine protein suitable for human consumption. Introduction The Greenhouse gas problem offers ocean engineering new opportunities in demonstrating environmental responsibility. The ocean provides the largest potential sink for atmospheric carbon dioxide but its uptake is restricted by a shortage of nitrogen and other nutrients. Phytoplankton forms the base of the photosynthetic carbon cycle in which atmospheric carbon dioxide is absorbed into the ocean. It plays a similar role to grass in the terrestrial food chain. By providing nutrients to the upper ocean photosynthesis can be enhanced, a process called ocean nourishment. The objective of the UN Framework Convention on Climate Change is to stabilise greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system. All Parties to the Convention are required to "promote and cooperate in the conservation and enhancement, as appropriate, of sinks and reservoirs of all greenhouse gases not controlled by the Montreal Protocol, including biomass, forests and oceans as well as other terrestrial, coastal and marine ecosystems". [4.1.d] One method proposed to make mitigation of carbon dioxide efficient is to have tradeable carbon credits. By producing a sink of carbon dioxide this commodity can be sold to offset the emissions of carbon into the atmosphere. If the sink of carbon dioxide can be produced more cheaply than the alternatives available to a carbon dioxide emitter (such as increased effiency or fuel switching) purchase of a carbon credit is attractive. The ocean nourishment process can produce carbon credits at a low cost when the feedstock is offshore natural gas. Jones and Cappelen-Smith (1999) talk about exploiting these fields. When one considers stranded natural gas exploited with a relocatable ocean nourishment plant, the economics are even more attractive. Primary production in the ocean Photosynthesis occurs in the upper sunlit zone of the ocean and combines carbon, nitrogen and phosphate (together with other micro-nutrients) in approximately the Redfield ratio. This ratio of C:N:P of 6.6:1:0.06 is a convenient generalisation. Phytoplankton die or are grazed upon by higher species in the food chain and the organic carbon and nitrogen are either recycled in the photic zone or fall through the water column to the deeper ocean.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score1.000

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)0.0120.001

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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations1
Published2000
Admission routes1
Has abstractyes

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