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Record W4408823231 · doi:10.5194/oos2025-1574

A Pilot Site Study to Remediate Low oxygen Conditions in Coastal Seas

2025· preprint· en· W4408823231 on OpenAlexaff
Patricia Handmann, Jakob Walve, David Austin, Douglas W.R. Wallace

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEnvironmental scienceOceanographyHydrology (agriculture)FisheryGeologyGeotechnical engineeringBiology

Abstract

fetched live from OpenAlex

Deoxygenation of the marine environment has been linked to: 1) global climate change, through decreasing gas solubility, increased stratification reducing deep ventilation and changes of the spatiotemporal and biogeochemical properties of ocean currents and 2) excessive nutrient input to the marine environment causing eutrophication. Both are linked to human activities1. The low oxic conditions are threatening biodiversity and the the marine ecosystem through reduction of habitat and altering biogeochemical processes in water and sediment2. This deterioration is significantly impacting regional economies, affecting thousands of jobs and billions of dollars3,4. Apart from limitations of greenhouse gas emissions and nutrient pollution, current conservation measures do not effectively address the impacts of reduced oxygen in the marine environment featureing large implementation time lags in projected outcomes5,6. Global offshore wind energy production potential is huge7 and often linked to the planned production of green hydrogen (e.g., 0.5 GW electrolyzer: ~210 t H2 d-1; ~1700 t O2 d-1). The oxygen could be used to mitigate anoxia, restore benthic habitat, reduce phosphorus loading, and suppress algal blooms in the coastal setting. Constant artificial oxygen injection (AO) could help combat hypoxia caused by circulation shifts, decreased deep mixing in autumn and winter and climate change8. In freshwater, except for coastal ocean aquaculture, small-scale AO is used, but larger-scale efforts are rare. Techniques were largely developed and implemented in US reservoirs9 (largest is 350t O2/d). AO for the marine environment has received little attention, likely due to the missing science basis in the marine environment, the investment costs (e.g. oxygen10) and/or lack of infrastructure and awareness. We want to present a first step toward the long-term objective of AO implementation in a larger scale setting. This first step is a small-scale AO pilot installation in a rather constrained marine environment. This effort was prepared by the BOxHy project (BSAP funded 10/2023 to 10/2024), generating a methodology and data analysis concerning a pilot study site for AO in the Baltic Sea with the perspective of upscaling the science/technology to basin wide scales. AO, as a novel innovative mitigation technique could be adapted to other anoxia-prone coastal environments after successful research and demonstration, closing major knowledge gaps and exploring the risks for unintended consequences. Exploring AO as a mitigation measure directly aligns with the principles of "prevention of harm” and the “precautionary approach” outlined in the “Declaration of Ethical Principles in Relation to Climate Change”11. 1 Breitburg 2018.2 Gregoire 2023.3 Pitcher 2021. 4 Dewar 2009. 5 STAC 2023. 6 Guidelines for Sea-Based Measures to Manage Internal Nutrient Reserves in the Baltic Sea Region. (2021) 7 IEA2019 Offshore wind outlook 2019: world energy outlook special report8 Wallace 2023. 9 Mobley 2019. 10 Stigebrandt 2022.11 Declaration of Ethical Principles in Relation to Climate Change. 2017 UNESCO Paris

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.273
Teacher spread0.251 · 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.

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
Published2025
Admission routes1
Has abstractyes

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