Economic valuation of suspended sediment and phosphorus filtration services by four different wetland types: A preliminary assessment for southern Ontario, Canada
Bibliographic record
Abstract
Abstract Wetlands are known for their water filtration (or purification) function. Although different wetland types differ in their filtration capacity, they are usually aggregated together in economic valuation studies. Here, we explicitly separate the valuation of the suspended sediment and phosphorus (P) filtration services of the four major wetland types—bogs, fens, marshes and swamps—found in southern Ontario, Canada. The areal extents of the four wetland types are derived from the Canadian Wetland Inventory (CWI) progress map, while the sediment accretion rate is used as the key variable regulating the suspended sediment and P filtration functions. Based on available literature data, we assess the relationship of the sediment accretion rate to wetland size. Because only weak positive correlations are found, we assign a mean (average) sediment accretion rate to each wetland type. The sediment accretion rates are combined with mean soil P concentrations to estimate Pretention rates by the wetlands. The replacement cost method is then applied to valuate the sediment and P filtration services. The unit values for both sediment and P retention decrease in the order: marshes > bogs ≈ swamps > fens. The total value of sediment plus phosphorus removal by all wetlands in southern Ontario amounts to $4.2 ± 2.9 billion per year, of which about 80% is accounted for by swamps. We further assess the costs of different options to offset the additional P loading generated in a hypothetical scenario whereby all wetlands are converted to agriculture. The results demonstrate that replacing the P filtration function of existing wetlands with conventional land management and water treatment solutions is not cost‐effective, hence reinforcing the importance of protecting existing wetlands.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".