Effects of landscape variables and season on reference water chemistry of coastal marshes in eastern Georgian Bay
Bibliographic record
Abstract
We surveyed 34 marshes in relatively pristine Precambrian Shield catchments in Georgian Bay and related water chemistry to a suite of landscape-level variables, including characteristics of the marsh and its drainage basin. The first landscape principal component (explained 48% of variation) ordered marshes along a gradient with high values corresponding to marshes with large watersheds that contain extensive upstream wetland and that receive relatively high precipitation inputs. This axis was negatively related to specific conductivity, pH, nitrate nitrogen, and SO42– concentrations and positively related to total phosphorus, colour, suspended solids, ammonia nitrogen, and summer dissolved organic carbon. Stepwise regression models built using catchment- and marsh-level variables explained up to 64% of the variation in water chemistry variables. Average precipitation and snowmelt inputs to the catchments were first to enter the majority of models, alone explaining up to 43% of the variation (in the case of water colour), while drainage area alone explained 44% of the variation in pH. Concentrations of catchment-derived constituents in marshes were highest in spring, reflecting greater loadings from the watersheds, while ionic strength was highest during summer, reflecting increased contributions form other sources (i.e., lake water).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".