ICES ‐ PICES session: Projected impacts of climate change on marine ecosystems, wild captured and cultured fisheries, and fishery dependent communities
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Conveners: Jon Hare (USA), John Pinnegar (UK), Myron Peck (Germany), Shin‐ichi Ito (Japan).CM 2017/A:263. Squid range expansion in a marine climate change ‘hotspot’ and why it matters to fisheries. Georg H. Engelhard, Jeroen van der Kooij, David A. Righton, Miranda C. Jones, John K. PinnegarCM 2017/A:321. A temperature dependent growth model for Dolphinfish in the Mediterranean: implications within a warming miniature ocean. Vicenç Moltó, Miquel Palmer, Beatriz Morales-Nin, Amina Besbes Benseddik, Mark Gatt, Sílvia Pérez, Francisco Alemany, Ignacio A. CatalánCM 2017/A:256. Influence of global warming on vertical distribution of sprat eggs in the Eastern Baltic and its possible impact on recruitment success. Andrei MakarchoukCM 2017/A:177. Modelling and predicting the impact of environmental variability on mackerel potential spawning habitat distribution. Baye Mbaye, Thomas Doniol-Valcroze, Stephane Plourde, Caroline Lehoux, Martin CastonguayCM 2017/A:107. Social and climate change vulnerability in fishing communities of the United States: An examination of shifting baselines. Lisa L. Colburn, Michael Jepson, Changhua Weng, Jonathan A. HareCM 2017/A:613. Modelling and mapping fishing pressure and fish standing stocks across Micronesia, and assessing the potential impacts of climate change. Alastair Harborne, A. L. Green, N. Peterson, M. Beger, E. Game, Y. Golbuu, P. Houk, T. Leberer, M. D. Spalding, B. M. Taylor, E. Terk, E. A. Treml, S. Victor, L. Vigliola, I. D. Williams, N. H. Wolff, P. zu Ermgassen, P. J. MumbyCM 2017/A:642. Spatial and temporal changes in future growth, recruitment, and biomass of Bering Sea walleye pollock, Gadus chalcogrammus. Kirstin K. Holsman, Elizabeth Siddon, Kerim Aydin, Anne Hollowed, Jim Ianelli, Ron Heintz, André PuntCM 2017/A:506. Modelling of Essential Fish Habitat for Mugil cephalus Based on IPCC Climate Scenarios in the Northwestern Pacific.CM 2017/A:317. Effects of temperature on black sea bass (Centropristis striata) metabolic rate and aerobic scope. E. SlesingerCM 2017/A:243. UK fisheries, climate change and storminess: how the harsh winter of 2013/14 can provide insights for the future. John K. Pinnegar, Roi MartinezCM 2017/A:639. Risks and Opportunities of Climate Change to the European Aquaculture Industry: The CERES approach. Myron A. Peck, Pauline Kamermans, Tom Doyle, Joao Ferreira, John PinnegarCM 2017/A:665. Projecting the state of future ecosystem services and international governance regimes for Svalbard: The REGIMES. Dorothy J. Dankel, Yajie Liu, Vicky Lam, Rachel Tiller, Elizabeth Nyman, Arne Eide, William ChengCM 2017/A:196. Habitat model to predict the spatial distribution of M. japanica bycatch species in the Eastern Pacific. Nerea Lezama-Ochoa, Martin Hall, Hilario Murua, Nick Vogel, Kelly Newton, Donal CrolCM 2017/A:539. Developing a generalized climate-niche modeling framework for commercially important species in a climatically altered marine environment: A Case Study with American lobster and Atlantic scallop in the Gulf of Maine. Kisei R. Tanaka, Michael P. Torre, Yong ChenCM 2017/A:660. Statistical Projections of Physical and Biological Oceanographic Indices off Newfoundland and Labrador. Guoqi Han, Eugene Colbourne, G. Maillet, Pierre PepinCM 2017/A:169. acidification effects on growth and development of Atlantic cod larvae in the Western Baltic and the Barents Sea. M. H. Stiasny, I.-B. Falk-Petersen, F. Jutfelt, F. H. Mittermayer, T.B.H. Reusch, N. K. Schnell, M. Sswat, R. Voss, C. ClemmesenCM 2017/A:506. Modelling of Essential Fish Habitat for Mugil cephalus Based on IPCC Climate Scenarios in the Northwestern Pacific. Sheng-Yuan Teng, Ming-An Lee, Nan-Jay Su, Kuo-Wei Lan, Yi Chang, Lung-Ching WuCM 2017/A:118. Some like it hot: regional insights into winners and losers of commercial fisheries in warming seas. Katherine M MaltbyCM 2017/A:582. Social-ecological climate vulnerability assessment framework: pilot implementation for Northeast U.S, fishing communities. Katherine E. Mills, Michael Alexander, Andrew Allyn, Lisa Colburn, Steve Eayrs, Bradley Franklin, Jonathan Hare, Troy Hartley, Mary Hudson, Brian Kennedy, Jonathan Labaree, Andrew Pershing, Justin Schuetz, James Scott, Jenny Sun, Eric ThunbergCM 2017/A:605. An emerging fishery at the northern edge of its range as a function of enhanced overwintering survival. Janet A. Nye, Emily Markowitz, Adam F. Younes, Maxwell SilversteinCM 2017/A:332. Forecasting future range of sea scallops using a trophically-linked species distribution model: Will climate change constrain scallop distribution in the Mid-Atlantic Bight?. Jui-Han Chang, Dvora Hart, Enrique Curchitser, Paula Fratatoni, Daphne Munroe, David Rudders, Burton ShankCM 2017/A:403. Understanding climate-induced changes in fish productivity to inform sustainable management. Adrien Tableau, Jeremy Collie, Richard BellCM 2017/A:640. The vulnerability of European seafood production to climate warming. Marie-Anne Blanchet, Raul Primicerio, Åslak Smalås, Michaela AschanCM 2017/A:342. A framework for quantifying the uncertainty budget when projecting climate change impacts on marine ecosystems: a case study of the southeastern Bering Sea. Anne B. Hollowed, Alan C. Haynie, Kristin Holsman, Kerim Aydin, Al Hermann, Wei Cheng, Jon Reum, Amanda FaigCM 2017/A:680. Global decline in coral reefs impacts the capacity for biodiversity & fisheries. Tyler D. Eddy, John F. Bruno, Vicky W. Y. Lam, Gabriel Reygondeau, William W. L. CheungCM 2017/A:218. Vulnerability Assessment of Fishing Resources of Azores to Climate Change. Cristiana A. Brito
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.018 |
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".