The Comparative Biodiversity of Seven Globally Important Wetlands. An Initiative from the Global Wetland Consortium (GWC)
Bibliographic record
Abstract
pg. 239 The comparative biodiversity of seven globally important wetlands. Junk WJ. pg. 240-253 Biological diversity of peatlands in Canada. Warner BG. Asada T. pg. 254-277 Species diversity in the Florida Everglades, USA: A systems approach to calculating biodiversity. Brown MT. Cohen MJ. Bardi E. Ingwersen WW. pg. 278-309 Biodiversity and its conservation in the Pantanal of Mato Grosso, Brazil. Junk WJ. da Cunha CN. Wantzen KM. Petermann P. Strussmann C. Marques MI. Adis J. pg. 310-337 Species diversity of the Okavango Delta, Botswana. Ramberg L. Hancock P. Lindholm M. Meyer T. Ringrose S. Sliva J. Van As J. VanderPost C. pg. 338-354 Biodiversity and its conservation in the Sundarban Mangrove Ecosystem. Gopal B. Chauhan M. pg. 355-373 Species diversity and ecology of Tonle Sap Great Lake, Cambodia. Campbell IC. Poole C. Giesen W. Valbo-Jorgensen J. pg. 374-399 Biodiversity of the wetlands of the Kakadu Region, northern Australia. Finlayson CM. Lowry J. Bellio MG. Nou S. Pidgeon R. Walden D. Humphrey C. Fox G. pg. 400-414 The comparative biodiversity of seven globally important wetlands: a synthesis. Junk WJ. Brown M. Campbell IC. Finlayson M. Gopal B. Ramberg L. Warner BG.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".