'When is enough, enough?' Methods for optimising, evaluating, and prioritising of marine data collection (co-sponsored by PICES)
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Conveners: J.H. Vølstad (Norway), Mike Armstrong (UK), Marie Storr-Paulsen (Denmark), Robyn Forrest (Canada).CM 2016/O:407. Alternative sample designs for the deployment of observers in the North Pacific. Craig H. FaunceCM 2016/O:69. Best-practice for long-term observations of total suspended particulate matter in coastal marine environments. Rolf Riethmüller, Michael Fettweis, Romaric VerneyCM 2016/O:564. Beyond cod and haddock: challenges for optimizing bottom trawl surveys for ecosystem monitoring. V. Trenkel, L. Pawlowski, M. RobertCM 2016/O:229. Calculating the economic benefits from the fisheries research surveys from indirect methodologies. Raúl PrellezoCM 2016/O:136. Can less be more? Effects of reduced frequency of surveys and stock assessments.Fabian Zimmermann, Katja EnbergCM 2016/O:519. Custom-tailored or fit for multi-purpose? Options for (re)designing North Sea fisheries surveys. Anne Sell, Julia Wischnewski, Holger Haslob, Francisco Marco-RiusCM 2016/O:521. Data quality in fisheries science. Laurent Dubroca, Norbert Billet, Joel Vigneau, Alice Vastel, Nicolas Goascoz, Anne-Sophie Cornou, Sébastien Démanèche, Jérome Weiss, Mathieu Merzereaud, Alastair Pout, Liz Clark, Mike Armstrong, Chun Chen, Kelig Mahe, Maria Hansson, Ana Ribeiro Santos, Eva Velasco, Christoph StranskyCM 2016/O:198. Definition of sampling priorities using Global Sensitivity Analysis and Management Strategy Evaluation. Dorleta Garcia, Raúl PrellezoCM 2016/O:166. Effect of shortening haul durations on a survey’s species richness estimates. M. Moriarty, A.F. Sell, V. M. Trenkel, Y. Vérin, S.P.R. GreenstreetCM 2016/O:511. Evaluating bias in an observer and self-sampling discard programme. Chun Chen, Ruben Verkempynck, Edwin van Helmond, Sebastian Uhlmann, Karin van der ReijdenCM 2016/O:263. Evaluating regional designs for the on-shore sampling of North Sea demersal fisheries. Alastair Pout, Liz Clarke, Ana Ribeiro Santos, Jon Elson, Patrik Börjesson, Mary Christman, Kirsten Birch Håkansson, Marie Storr-PaulsenCM 2016/O:238. Evaluation of sampling designs for data-limited populations of Pacific salmon in Canada. Carrie A. HoltCM 2016/O:117. Geostatistical index standardization improves the performance of stock assessment model: an application to northern shrimp in the Gulf of Maine. Jie Cao, James T. Thorson, R. Anne Richards, Yong ChenCM 2016/O:265. How do surveys’ variances affect the assessment and management of the Bay of Biscay anchovy?. Andres Uriarte, Sonia Sanchez, Leire IbaibarriagaCM 2016/O:148. How does minimum sampling size relate to statistical power in stomach content analysis?. L. Lopez‐Lopez, I. PreciadoCM 2016/O:177. Identification of optimal sub-sampling approach for video trawl survey analysis. Travis M. LoweryCM 2016/O:100. Impact of splitting decisions on reported aggregated catch of two sympatric redfish (Sebastes spp) species on stock status and harvest advice. Daniel E DupliseaCM 2016/O:228. Improving the analytical assessment of fish stocks by providing parameters of data quality via InterCatch. Jose Castro, Laurent Dubroca, Jose Rodriguez, Eva Velasco, Paz SampedroCM 2016/O:353. Inferring the annual, seasonal and spatial distributions of marine species from combined research and commercial vessels’ catch rates. Pierre Bourdaud, Morgane Travers-Trolet, Youen Vermard, Xochitl Cormon, Paul MarchalCM 2016/O:127. Little data, big impact: using simple data protocols to achieve real-time management goals. B.L. Wright, C.E. O’Keefe, S.X. CadrinCM 2016/O:170. Modeling length distribution by commercial size category to estimate species catch length composition for stock assessment. M. Azevedo, C. Silva, J.H. VølstadCM 2016/O:583. Optimising sampling intensity though observation, not simulation: An example from an at-sea, demersal fishery sampling programme. Pieter-Jan Schön, Mathieu G LundyCM 2016/O:106. Optimizing data collection processes for industry collected gear selectivity data. Tiago Malta, Jordan Feekings, Bent Herrmann, Ludvig Ahm KragCM 2016/O:235. Optimizing otoliths sampling design in fishery-independent surveys for stock assessments. Gwladys Lambert, Thomas Hesler, Pete Hulson, Anne HollowedCM 2016/O:430. Performance of alternative assessment methods for Pacific Cod (Gadus macrocephalus) in British Columbia: a difficult-to-age species with highly uncertain productivity. Robyn E. Forrest CM 2016/O:569. Presenting the MarinEye project –Development and validation of a prototype for multitrophic oceanic monitoring. Cátia Bartilotti, Antonina dos Santos, Raquel Marques, Alexandra Silva, Catarina Churro, Sónia Cotrim, Hugo Ribeiro, Ana Paula Mucha, Maria de Fátima Carvalho, C. Marisa R. Almeida, Isabel Azevedo, Sandra Ramos, Teresa Borges, Sérgio Leandro, André Dias, Eduardo Silva, Hugo Ferreira, Ireneu Dias, José Miguel Almeida, Luís Torgo, Nuno Dias, Pedro Jorge, Alfredo Martins, Catarina MagalhãesCM 2016/O:612. ptyx : data compilation at the edge of automation. Laurent Dubroca, Joel Vigneau, Anne-Sophie Cornou, Sébastien Démanèche, Jérome Weiss, Mathieu Merzereaud, Youen Vermard, Marie Savina-Rolland, Ching-Maria Villanuova, Ivan Shlaich, Lionel Pawlowski, Marianne Robert, Muriel Lissardy, Gilles Morandeau, Mickael Drogou, Spyros Fifas, Hélène Gadenne, Pascal Lorance, Claire Saraux, Tristan Rouyer, Florence GontrandCM 2016/O:491. Quantifying optimal sampling effort for estimating recruit abundance for juvenile anadromous alewife. Matthew T. Devine, Allison H. Roy, Andrew R. Whiteley, Adrian JordaanCM 2016/O:REDUS - an integrated approach to quantifying and communicating uncertainty in stock assessment and management advice. Erik Olsen, Jon Helge Vølstad, Knut Korsbrekke, Espen Johnsen, Nils Olav Handegard, Gulborg Søvik, Daniel Howell, Cecilie HansenCM 2016/O:142. Statistical analysis of the sampling design: FishPi case study on the biological sampling of the European hake fishery. Jose Castro, Nuno Prista, Lucia Zarauz, Jose Rodriguez, Manuela Azevedo, Laurent Dubroca, Alastair Pout, Joao Pereira, Chun Chen, Hans Gerritsen, Jon Elson, Ana Ribeiro, Patrik Börjesson, Kirsten Birch Håkansson, Sofie NimmegeersCM 2016/O:145. Strategic use of uncertainty: Regional comparison of fisheries cooperation in the North Pacific and Northeast Atlantic. Robert Blasiak, Jessica SpijkersCM 2016/O:440. Technological tools for optimising data collection and management. Inês Farias, Aida Campos, Artur Rocha, Gabriel David, Guida Camacho, João Castro, Miriam Tuaty Guerra, Ricardo Amorim, Victor Henriques, Antonina dos SantosCM 2016/O:206.The end of reporting deadlines? Optimising dataflow by harvesting – a case study using open source REST-server technology from SMHI in corporation with ICES. Patrik StrömbergCM 2016/O:435. Using simulation to assess the effects of various sampling schemes on estimates of population characteristics of fish from bottom-trawl surveys. Paul M. Regular, Fran MowbrayCM 2016/O:611. When is necessary, already available?. Joel Vigneau, Laurent Dubroca, Youen Vermard, Anne-Sophie Cornou, Sébastien Démanèche, Jérome WeissCM 2016/O:589. Which species to sample? An objective method for the selection of species when sampling fish on a market. Liz Clarke, Alastair Pout, Stephanie Sweeting, Fanyan Zeng, Lynette Ritchie, Peter Clark, Mandy Gault, Harriet Cole
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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 | 0.218 | 0.407 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier 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".