A Pilot Site Study to Remediate Low oxygen Conditions in Coastal Seas
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".