Ocean basin-scale research and management: challenges and opportunities
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author. Conveners: J. Murray Roberts (United Kingdom), Ellen Kenchington (Canada).CM 2018/G:580. Deep-Sea Marine Genetic Resources: review and analysis of existing MPAs management. Potential implication for future MSP practices. Elisabetta Menini, Cindy Lee Van Dover, Helena Calado, Roberto Danovaro, Elisabetta ManeaCM 2018/G:166. Data portals to support marine management at regional sea’s scale – the coastMap approach. Linda Baldewein, Marcus Lange, Ulrike Kleeberg, Dietmar SauerCM 2018/G:100. Investigating unit stock boundaries of the deep-water rose shrimp by modelling larvae aggregation in the Strait of Sicily (Central Mediterranean Sea). Giovanni Quattrocchi, Matteo Sinerchia, Francesco Colloca, Fabio Fiorentino, Germana Garofalo, Andrea CuccoCM 2018/G:636. Challenges for sustainable monitoring and evaluation of the EU Marine Strategy Framework Directive in the Atlantic offshore waters: the iFADO project. Campuzano Francisco, Borges Maria de Fatima, Oliveira Paulo, Dabrowski Tomasz, Groom Steve, Ruiz-Villarreal Manuel, Brotas VandaCM 2018/G:378. Dynamic management approaches for marine mammals. Anita Gilles, Sacha Viquerat, Geert Aarts, Elizabeth A. Becker, Ute Daewel, Steve C.V. Geelhoed, Jan Haelters, Jacob Nabe-Nielsen, Meike Scheidat, Corinna Schrum, Ursula Siebert, Signe Sveegaard, Floris M. van Beest, Rob van Bemmelen, Karin A. ForneyCM 2018/G:534. The Atlantic Ocean a shared resource: Can MPA networks be used as a tool to improve a sea-basin conservation strategy? Marques M., Alves F. L.CM 2018/G:594. A comparison of assessment methods and time scales of biodiversity indicators in the scope of the MSFD in North-Eastern Atlantic: impacts for intercalibration and GES classification. Machado I., Costa J.L, Pasquaud S., Cabral H.CM 2018/G:508. Ocean basin-scale distribution models in the ATLAS project. The challenges of big thinking. José Manuel González-Irusta, Carlos Domínguez-Carrió, Marina Carreiro-Silva,Telmo MoratoCM 2018/G:567. Biophysical drivers of ocean-wide connectivity in a holoplanktonic zooplankton. C. Chang, E. Goetze, A.B. NeuheimerCM 2018/G:568. Spawning time adaptation allows fish to meet their food across an ocean basin. A.B. Neuheimer, B.R. MacKenzie, M.R. PayneCM 2018/G:86. Spatiotemporal features of the population dynamics of cutlassfish (Trichiurus spp.) with their relations to the regional environment in the northwest Pacific Ocean. Baochao Liao, Yan Jiao, Xiujuan Shan, Abdul Baset, Qun Liu
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".