The practical use of ecosystem indicators for decision-making
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Conveners: Jamie C. Tam (Canada), Alida Bundy (Canada), Laura Uusitalo (Finland), Annukka Lehikoinen (Finland).CM 2017/H:162. Indicators for Ecosystem-Based Fisheries Management: 20 years of research. Alida Bundy, Lynne Shannon, Ekin Akoglu, Daniela Banaru, Marta Coll, Caihong Fu, Beth Fulton, Arnaud Gruss, Cecelie Hansen, Ghassen Halouani, Sigrid Lehuta, Ricardo Oliveros Ramos, Morgane Travers-Trolet, Yunne Jae ShinCM 2017/H:537. Defining ecosystem thresholds for human activities and environmental pressures in the California Current. Jameal F. Samhouri, Kelly S. Andrews, Gavin Fay, Chris J. Harvey, Elliott L. Hazen, Shannon M. Hennessey, Kirstin Holsman,Mary E. Hunsicker, Scott I. Large, Kristin N. Marshall, Adrian C. Stier, Jamie C. Tam, Stephani G. ZadorCM 2017/H:659. Machine learning applications in marine management: evaluating complex indicator-pressure relationships across fishpopulations. Annukka Lehikoinen, Laura Uusitalo, Lena Bergström, Ulf Bergström, Andreas Bryhn, Heikki Peltonen, Ronny Fredriksson, Jens OlssonCM 2017/H:149. Using Bayesian machine learning techniques for addressing the role of natural and human-induced drivers for coastal fishindicators in the Baltic Sea. Jens Olsson, Jens Olsson, Annukka Lehikoinen, Lena Bergström, Ulf Bergström, Andreas Bryhn, Göran Sundblad, RonnyFredriksson, Laura UusitaloCM 2017/H:198. INDperform: an R package for validating ecological state indicator performances. Saskia A. Otto, Alexander Keth, Réné Plonus, Steffen Funk, Christian MöllmannCM 2017/H:151. A risk-reward framework to identify thresholds for indicator assessment that are useful to management. Robert B. Thorpe, Georg H. Engelhard, Christopher P. LynamCM 2017/H:589. Coupled food-web indicator models and scenario simulations identify robust indicators guiding management actions. Martina Kadin, Michele Casini, Maria A. Torres, Thorsten Blenckner, Anna Gårdmark, Saskia A. OttoCM 2017/H:658. Measuring the response of ecosystem indicators to events. Christopher R. Kelble, Mandy Karnauskas, Neda Trifonova, Seann ReganCM 2017/H:77. Collaboration is the key to success: developing pelagic habitat indicators by working across the science-policy interface. Abigail McQuatters-Gollop, Anais Aubert, Isabelle Rombouts, Felipe Artigas, Alexandre Budria, David Johns, Clare OstleCM 2017/H:492. A social-ecological approach to estimate fishers' resilience: A case study from Brazil. Monalisa R O da Silva, Maria G Pennino, Priscila F M LopesCM 2017/H:416. Decision support for sustainable seafood production. Petter Olsen, Alan Baudron, Bo Lærke, Kåre Nolde Nielsen, Mika Rahikainen, Andre TapadinhasCM 2017/H:88. Overfishing as a legitimate management goal. Eckhard BethkeCM 2017/H:120. Plankton as prevailing conditions: a surveillance role for plankton indicators within the Marine Strategy Framework Directive. Jacob Bedford, Abigail McQuatters-Gollop, David JohnsCM 2017/H:657. The Use of Hidden Variable Dynamic Models to Detect Functional Changes in the Gulf of Mexico Marine Ecosystem. Neda Trifonova, Christopher Kelble, Mandy Karnauskas
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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 | 0.104 | 0.251 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".