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Record W1841470610 · doi:10.1007/s13280-015-0701-5

Digital conservation: An introduction

2015· article· en· W1841470610 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAMBIO · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsComputer scienceEnvironmental science

Abstract

fetched live from OpenAlex

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 Soderstrom, 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 Buscher, 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 Tromso, 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 fur 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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.204
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it