Bottom-up approaches: the contribution of marine benthos to management, conservation and monitoring; taking stock, and setting research direction
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author. Conveners: Silvana Birchenough (United Kingdom), Ingrid Kröncke (Germany), Steven Degraer (Belgium).CM 2018/F:126. Risk, politics, and science: a new approach to monitoring UK marine benthic ecosystems. Hayley Hinchen, Yessica Griffiths, Henk van ReinCM 2018/F:527. Bottom-up approaches: the contribution of marine benthos to management,conservation and monitoring, taking stock and setting research direction. Maarten Platteeuw, Maarten de Jong, Aylin Erkman, Suzanne Lubbe, Marijke Warnas, Ingeborg van SplunderCM 2018/F:380. MARLIN – a large-scale/high resolution information system as a backbone for marine management. Gregor von Halem, Jennifer Dannheim, Jan Beermann, Mathias Robeck, Anne ElsnerCM 2018/F:599. Global hotspots of seahorse richness and extinction risks. Rui Rosa, Inês Bom, Catarina Santos, Catarina Frazão-SantosCM 2018/F:318. Using underwater video to assess megabenthic community vulnerability to trawling in the Grande Vasiere (Bay of Biscay). MERILLET Laurène, MOUCHET Maud, ROBERT Marianne, SALAUN Michèle, VAZ Sandrine, KOPP DorothéeCM 2018/F:429. Comparison of functional and structural long-term variability of south-eastern North Sea macrofauna communities in relation to environmental parameters. Julia Meyer, Ingrid KrönckeCM 2018/F:128. Trophic positions of coastal consumers, determined using trophic mass-balance modelling and stable isotope analyses. Rasa Morkūnė, Egidijus Bacevičius, Artūras Razinkovas-BaziukasCM 2018/F:310. Benthic epifauna long-term trends in the North Sea from 1998 to 2018. Hermann Neumann, Ingrid KrönckeCM 2018/F:383. Recent findings about benthic non-indigenous species in the ports of the southern part of the Baltic Sea. Mara Harju, Monta Grudcina, Solvita StrakeCM 2018/F:99. Intertidal salt-marsh creeks: benthos-rich feeding grounds for fish. Julia Friese, Axel Temming, Andreas DänhardtCM 2018/F408:. Effects of fisheries closures on seafloor integrity: a modelling example from the German part of the North Sea. Diekmann, R., Neumann, H., Rambo, H., Kröncke, I., Stelzenmüller, V.CM 2018/F:. Evaluating the risk of vulnerable marine ecosystems in arctic and sub-arctic waters to commercial fisheries. J.M. Burgos, L. Buhl-Mortensen, P. Buhl-Mortensen, S.H. Ólafsdóttir, P. Steingrund, S.Á. RagnarssonCM 2018/F:283. Spatial variability in benthic community structure along benthoscapes in offshore Canadian waters: implications for monitoring. Myriam Lacharité, Craig J. BrownCM 2018/F:162. Estimating sensitivity of seabed habitats to disturbance by bottom trawling based on the longevity of benthic fauna. Adriaan D. Rijnsdorp, Stefan G. Bolam, Clement Garcia, Jan Geert Hiddink, Niels T. Hintzen, P. Daniel van Denderen, Tobias van Kooten
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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 | 0.085 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".