The spatial organization of ecosystem services in river‐floodplains
Bibliographic record
Abstract
Abstract River‐floodplains are hotspots for many ecosystem services (ES), and thus, understanding how these services are spatially organized along river systems is essential. General principles from river‐floodplain ecology may provide guidance for understanding these spatial patterns, yet such concepts have rarely been incorporated into spatial assessments of ES. Using a lens of riverine concepts, we contrasted how floodplain ES capacity and diversity (orchard production, forage production, carbon storage, paddle route quality, fish capacity) vary with longitudinal river‐floodplain position. High spatial resolution aerial photography (2006) facilitated detection of floodplain features contributing to the production of ES. We also determined how river reach types are linked to production of ES. We found that ES capacity varied considerably with longitudinal position and reach type. Agricultural capacity was concentrated in lower reaches, high‐quality paddle routes in middle‐lower reaches, and fish capacity and carbon storage in upper reaches. Furthermore, the highest diversity of ES was concentrated in the lowland floodplain reaches. Our results suggest river‐floodplain concepts can improve spatial assessments of ES, increase our understanding of the relationships among biological features and ES, and thus help us better manage some of the key ES trade‐offs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".