An integrated approach to model wetland dynamics in a changing landscape: A case study from South Africa.
Bibliographic record
Abstract
Wetlands have received an unprecedented amount of scientific and public attention during the last years. This is mainly caused by the worldwide loss and degradation of these highly sensitive habitats. Their conservation and sustainable development have been considered in a variety of national and international programmes and activities (Clean Water Act 1977, National Water Act 1998, RAMSAR 1971, UNDP etc.). As a result of the increasing awareness, the hydrological, hydro- and biochemical and eco-logical functions of wetlands as well as their importance for the water and nutrient cycle have been investigated in a variety of different studies. The ongoing research can be summarized as following: i) The majority of inland wetland studies are focused on the humid regions of the northern hemisphere (Europe, USA, Canada). ii) A variety of different definitions and classification systems for wetlands have been developed regarding specific topics and research needs. iii) The complexity of both the process dynamics within the wetlands and their correspondence with the surrounding environments is realised; how-
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".