Bibliographic record
Abstract
We thank all participants of the Digital Conservation Conference (May 2014, Aberdeen, UK) for laying the foundations of this Special Issue, Annie Robinson and Gina Maffey for their crucial input into the conference, all authors for contributing their work to the issue, and Bo Söderström, Ambio’s Editor-in-Chief, for the large amount of skill, energy and time invested. All papers have been rigorously peer-reviewed. We are very grateful to the 36 referees listed below: Steve Albon, the James Hutton Institute, Aberdeen, UK; Arjun Amar, University of Cape Town, South Africa; Karen Anderson, University of Exeter, UK; Debora Arlt, Swedish University of Agricultural Sciences, Uppsala, Sweden; Bob Askins, Connecticut College, New London, USA; Tom August, Centre for Ecology and Hydrology, Wallingford, UK; Iain Bainbridge, Scottish Natural Heritage, Edinburgh, UK; Elizabeth Boakes, University College London, UK; Bram Büscher, University of Wageningen, the Netherlands; Guillaume Chapron, Swedish University of Agricultural Sciences, Riddarhyttan, Sweden; Heather Doran, University of Aberdeen, UK; Rosaleen Duffy, University of London, UK; Gorry Fairhurst, University of Aberdeen, UK; Ioan Fazey, University of Dundee, UK; Rachel Finn, Trilateral Research and Consulting, London, UK; John Fryxell, University of Guelph, Canada; John Hallam, University of Southern Denmark, Odense, Denmark; Sandra Hamel, University of Tromsø, Norway; Maarten Jacobs, University of Wageningen, the Netherlands; Lucas Joppa, Microsoft Research, Redmond, USA; Steve Kelling, Cornell University, Ithaca, USA; Kerry Kilshaw, University of Oxford, UK; Christiane Lellig, Stratageme, Agentur für Social Change, Aldershot, UK; Nick Littlewood, the James Hutton Institute, Aberdeen, UK; Gina Maffey, University of Aberdeen, UK; Mariella Marzano, Forest Research, Roslin, UK; Fran Michelmoore Root, Northern Rangelands Trust, Isiolo, Kenya; Steve Redpath, University of Aberdeen, UK; Mark Reed, Birmingham City University, UK; Chris Sandbrook, UNEP-World Conservation Monitoring Centre, Cambridge, UK; Lisa Sargood, Digital Strategy & Innovation, Bristol, UK; Bill Sutherland, University of Cambridge, UK; Chris Thaxter, British Trust for Ornithology, Thetford, UK; Jean-Pierre Tremblay, Laval University, Quebec City, Canada; Audrey Verma, University of Aberdeen, UK; Jerry Wilson, Royal Society for the Protection of Birds, Edinburgh, UK.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".