Land-use Drives Seasonal Riverine Si Cycling at the Landscape Scale
Bibliographic record
Abstract
Silicon (Si) is an important element in the environment and is required for diatom production. Bedrock type, weathering rate and terrestrial vegetation are factors known to influence dissolved Si fluxes at the landscape scale, however the combined effect of these factors, along with anthropogenic influences on Si concentration and seasonal Si cycling is yet unknown. Using the provincial water quality monitoring network dataset (PWQMN) provided by the Ontario Ministry of Environment (Canada), Satellite data from The Ontario Land Cover Database (OMNR), we tested several factors that may influence dissolved silica (DSi) concentration and the annual DSi concentration range (as a proxy for seasonal cycling) for 79 river and stream monitoring stations within 53 distinct sub-watersheds in Ontario, Canada, for the years 2005 to 2011 in single and multivariate analyses. Our results indicate that the annual average DSi concentration is not affected by land-use type. The annual range in DSi, however, is positively influenced by the percent of land under agriculture, and negatively influenced by the amount of land covered by forests. Because average DSi is not affected by land-use, increased N and P loads associated with agricultural activity mean lowered Si:N and Si:P ratios in river water. Increased annual DSi range implies changes to both the timing and the quantity of Si delivered to downstream coastal environments, which may have implications for seasonal diatom production and carbon sequestration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".