Marine aquaculture in a changing ocean
Bibliographic record
Abstract
Conveners: Ben Halpern (USA), Halley Froehlich (USA), Gesche Krause (Germany). No abstracts are to be cited without prior reference to the author.Aquaculture’s place in the landscape of food’s environmental impact: Benjamin S. Halpern, Caitlin D. Kuempel, Halley E. Froehlich, Julia L. Blanchard, Lex Bouwman, Richard S. Cottrell, Jessica A. Gephart, Nis Sand Jacobsen, Peter B. McIntyre, Marc Metian, Daniel Moran, Kirsty L. Nash, Johannes Tobben, and David R. Williams.Are there terrestrial limits to marine aquaculture? Julia L. Blanchard, Richard S. Cottrell, Reg A. Watson, Duncan D. Cameron, et al.Social and ecological factors influencing the sustainability of intertidal clam aquaculture: Molly Miller, Teresa R. Johnson.Blue growth and aquaculture development. What about its social acceptability: Katia Frangoudes , Marianna Cavallo, Pascal Raux, José Perez.Offshore aquaculture and wind farms in the context of ecosystem-based marine spatial management: a micro-planning approachs: Antje Gimpel, Vanessa Stelzenmueller, Maximilian F. Schupp, Bela H. Buck.Does Integrated Multitrophic Aquaculture (IMTA) mean extensive or intensive systems? Mark Johnson.Ocean food systems: a transdisciplinary, ecosystems ecology approach to marine aquaculture: Barry A. Costa-Pierce, Adam St. Gelais, Zachary Miller-Hope, Kristina Snuttan Sundell, Helgi Thorarensen, Ögmundur Knútsson.Food web approach towards sustainable bivalve aquaculture: Carrie J. Byron, Adrianus Both, Carissa E. Maurin, Eric J. Chapman.Mechanistic derived quantities to inform acquaculture activities in a changing Mediterranean sea: M Cristina Mangano, Giacoletti A., Sarà G.Marine aquaculture under climate change impacts: Halley E. Froehlich, Benjamin S. Halpern, Rebecca R. Gentry, Jamie C. Afflerbach, Melanie Frazier.Sustainable aquaculture: Assessing & mitigating the risks of Harmful Algal Blooms: A Ross Brown, Martin Lilley, Jamie Shutler, Chris Lowe, Yuri Artioli, Ricardo Torres, Charles R. Tyler.Long-term environmental effects of pearl farming and climate change induced changes in phytoplankton productivity in north-western Australia: Dongyan Liu, Zineng Yuan, John Keesing.American lobster movement around salmon farms in eastern Canada: Chris McKindsey, Annick Drouin, Shawn M. C. Robinson, Émilie Simard.Predicting mariculture’s impacts on wild fisheries: Jessica Couture, Steve Gaines, Ben Halpern.A SWOT analysis of the multi-use of offshore wind farms and marine aquaculture: Maximilian Felix Schupp , Gesche Krause, Vincent Onyango, Bela H. Buck.Does decoupling aquaculture from the ocean improve sustainability? Kristina Bergman, Patrik Henriksson, Sara Hornborg, Max Troell, Malin Jonell, Friederike Ziegler.Capturing Sustainable Development Goals dimensions of transformative marine aquaculture in Europe: Gesche Krause, Suzannah-Lynn Billing, John Denis, Lucia Fanning, Molly Miller, Nardine Stybel, Selina M. Stead, Wojciech Wawrzynski.Can we uncouple fed aquaculture from fish-based feeds? Richard S. Cottrell, Julia L. Blanchard, Benjamin S. Halpern, Marc Metian, Halley E. Froehlich.Barriers to aquaculture growth: social, regulatory, and spatial constraints in Virginia’s eastern oyster aquaculture industry: Jennifer Beckensteiner, Andrew M Scheld, David M Kaplan.Vulnerability of European aquaculture and fisheries to a changing ocean: Catarina Frazão-Santos, Tundi Agardy, Manuel Barange, Larry B. Crowder, Charles N. Ehler, Michael K. Orbach, Francisco Andrade, Helena Calado, Carina Vieira da Silva, Renato Rosa, Maria A. Cunha-e-Sá, Elena Gissi, Hans-Otto Pörtner, Rui Rosa.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.011 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".