A spatially explicit model of iron loading to lakes
Bibliographic record
Abstract
Terrestrial and aquatic ecosystems are intimately linked by the export of elements from watersheds. Although export is influenced by land cover within watersheds, few models evaluate how the spatial configuration of land cover influences loading. In this study we examined spatial variation of land cover at a 10 X 10 m resolution by developing a mass balance, maximum likelihood model of lake iron (Fe) concentrations in 93 watersheds. The model estimated lake iron concentrations based on loading, within-lake processes and losses. Two models were developed. One considered loading from eight land cover types, whereas the second model included the distance of each grid cell to account for Fe losses along flow paths to the lake. In-lake production and losses were accounted for as a function of lake area, water color, and discharge. If we treated watersheds as homogeneous source areas, export was estimated as 450 mg Fe m-2 yr-1; however, in spatial models export varied from negligible to 5,400 mg Fe m-2 yr-1 based on differential loadings from eight cover types. Accounting for losses of Fe based on distance from the lake did not improve the model. Although areal export of Fe was greater from wetlands, upland forests dominate the landscape and thus accounted for on average 75% of the total Fe load. Fe losses from lakes were primarily regulated by discharge; however, water color and lake depth were also important. Overall, the analysis revealed that lake Fe concentrations are related to land cover based on strong differential Fe loadings.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".