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Record W4285557832 · doi:10.57348/00000124

Oyster aquaculture using seagrass beds as a climate change countermeasure

2021· preprint· en· W4285557832 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.

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2021
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsSeagrassOysterCountermeasureAquacultureFisheryClimate changeEnvironmental scienceOceanographyGeologyFish <Actinopterygii>EcologyEcosystemEngineeringBiology

Abstract

fetched live from OpenAlex

In the framework of the Sustainable Development Goals (SDGs) led by the United Nations, coastal management methods are required to achieve both sustainable food production and environmental conservation as a climate change countermeasure. Oyster farming is an important food production method now being developed in coastal areas around the world. Recently, climate change has caused several negative effects on oyster aquaculture such as poor spat collection due to oligotrophication, ocean acidification, and poor spat growth and survival due to frequent anoxic events derived from high seawater temperature. The oysters cultivated in many regions of the world are intertidal species inhabiting intertidal zones such as sandy/muddy tidal flats and estuaries, where seagrass beds are often distributed in adjacent lower intertidal and subtidal areas. Seagrass vegetation is one of the most important ecosystems functioning as a countermeasure for global climate change. Not only does it mitigate greenhouse gas emissions by sequestration and storage of blue carbon derived from atmospheric CO2, but it also functions as an adaptation measure providing a buffering function against ocean acidification and water quality improvement. Based on the concept of aquaculture supported by natural ecosystem interactions between oysters and seagrass beds, our project examined whether aquaculture techniques that take into account both mitigation and adaptation to climate change are effective for both sustainable use of coastal areas and environmental conservation. We conducted field experiments in both the French Mediterranean Sea and the Seto Inland Sea of Japan to clarify the effect of eelgrass beds on (1) natural oyster spat collection and (2) growth and survival of oyster spat. The results of our experiments revealed that spat recruitment was significantly higher in areas without eelgrass distribution, while spat growth and survival rate after the settlement were significantly higher in eelgrass beds even when anoxic events occurred in the study areas. Therefore, our results indicate a possibility that seagrass vegetation contributes to sustainability of oyster aquaculture by mitigating environmental degradation during cultivation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.089
GPT teacher head0.288
Teacher spread0.198 · 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