Desktop classification of inland wetlands for systematic conservation planning in data‐scarce countries: mapping wetland ecosystem types, disturbance indices and threatened species associations at country‐wide scale
Bibliographic record
Abstract
Abstract Data sets on wetlands required for the representation of aquatic ecosystem biodiversity and systematic wetland conservation planning are typically not available or are inadequate, particularly at country‐wide scale, which hinders conservation planning. The improvement in hierarchical classification systems and increased availability of broad‐scale data sets offers new opportunities to overcome these limitations. This study demonstrates replicable methods for classifying wetland ecosystem types and condition country‐wide using broad‐scale data sets in data‐scarce countries. A country‐wide data set, compiled primarily using remote sensing techniques, was combined with regional and landscape‐setting data sets to reflect the ecological and geomorphic biodiversity of wetlands. Geographical Information Systems (GIS) were employed to model wetland types, disturbance indices and identify priority wetlands through threatened faunal species associations using existing data. Accuracy of the national data was assessed through a congruency with two local data sets. Most of the 1 680 306 ha of inland wetlands were classified as Natural (80%), of which the majority were located on Valley Floors (68%). However, the national data were found only to represent 54% of wetlands mapped at a local scale, and comparison with local data showed inaccuracies in the types and condition classifications. Problems regarding spatial data quality and scale are discussed and suggestions for improvement are provided. The desktop classification steps can be reproduced easily for other data‐scarce countries. Data sets on freshwater ecosystems can assist in raising awareness and influence policy at a national scale. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".