Arctic ecosystem services: challenges and opportunities (co-sponsored by AMAP, EU-PolarNet, and ICES)
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Conveners: Candace Nachman (USA), Susanne Kortsch (Norway).CM 2016/P:248. A Bioeconomic Model of Ocean Acidification Challenges in the Baffin Bay/Davis Straight Shrimp Fishery. Brooks A. Kaiser, Lars Ravn-JonsenCM 2016/P:122. An overview of the adequacy of Arctic sea basin data. Belinda J. Kater, Nathalie Steins, Martine J. van den Heuvel‐Greve, Peter Thijsse, CJ Beegle‐Krause, Oscar Bos, Bart Grasmeijer, Le Griffin, Eline van Onselen, Harriet van Overzee, Gerjan Piet, Andrea Sneekes, Arjan Tuijnder, Pepijn de Vries, Jan Tjalling van der WalCM 2016/P:624. Ballast water of domestic ships as a pathway for the introduction of non-indigenous mesozooplankton in coastal Nunavik, Canada. Pascal Tremblay, André Rochon, Gesche Winkler, Kimberly Howland, Nathalie Simard, Sarah BaileyCM 2016/P:445. Benthic non-indigenous species in ports of the Canadian Arctic: risks associated with global warming and shipping activity. Kimberly Howland, Jesica Goldsmit, Philippe Archambault, David Barber, Guillem Chust, George Liu, Jennifer Lukovich, Chris McKindsey, Ernesto VillarinoCM 2016/P:644. Climate change impacts on the ecosystem services of Arctic cod (Boreogadus saida). Benjamin J. Laurel, Louise A. CopemanCM 2016/P:178. Cod response to past and current warm phases in the seas of Iceland, a time series analysis. Marcos Llope, Niall McGinty, Joël Durant, Leif C. Stige, Guðrún Marteinsdóttir, Nils Chr. StensethCM 2016/P:450. Modelling spatio-temporal variation of surface hydrography in an Arctic shelf sea. Jussi Mäkinen, Jarno VanhataloCM 2016/P:129. Pink salmon as sentinels for climate change in the Arctic. Ed Farley, Wess Strasburger, Jeanette Gann, Kris CiecielCM 2016/P:433. Regional heterogeneity in climate change impacts on the living marine resources of the Arctic. Anne B. Hollowed, Wei Cheng, Harald Loeng, Libby Logerwell, Franz Mueter, James ReistCM 2016/P:268. Socio-economic impacts of ocean acidification and warming on Barents Sea Cod. Martina H. Stiasny, Martin Hänsel, Catriona Clemmesen, Flemming Dahlke, Felix H. Mittermayer, Martin Quaas, Thorsten Reusch, Daniela Storch, Rudi VossCM 2016/P:502. Structure and resilience of the benthic food web across the Canadian Arctic Ocean and the Chukchi sea. Noémie Friscourt, Christian Nozais, Philippe ArchambaultCM 2016/P:647. SYMBIOSES - a practical risk management tool to integrate fisheries and hydrocarbon activities in the Lofotens and Barents Sea, Norway. Daniel Howell, JoLynn Caroll, Frode VikebøCM 2016/P:405. The Missing Middle: The Need for International Collaboration to Fill Gaps in Central Arctic Ocean Science. Henry P. Huntington, Thomas Van Pelt, Hyoung Chul ShinCM 2016/P:280. Towards quantitative oil spill risk assessment in the Arctic sea areas. Maisa Nevalainen, Inari Helle, Jarno VanhataloCM 2016/P:556. Trophodynamics of Atlantic cod (Gadus morhua) on the Greenland continental shelf 2006-2010 and Spitsbergen 2010. Karl‐Michael Werner, Sophia Kochalski, Jerome Chladek, Corinna Schendel, Heino O. FockCM 2016/P:559. Unique insights from historical fisheries survey logbooks in the Arctic. John K. Pinnegar, Bryony L. Townhill, Georg H. Engelhard
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.019 |
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".