Measuring function and structure of urban headwater streams with citizen scientists
Bibliographic record
Abstract
Abstract Headwater streams accumulate, process, and export organic materials for use in downstream environments. Decomposition of organic material, an important ecosystem function, may be sensitive to land cover changes in urbanizing regions since headwater stream processes tend to be tightly coupled with riparian and catchment characteristics. Headwaters represent 70–80% of total stream length in watersheds but are disproportionately converted to drainage infrastructure or buried with urban development. Cumulatively, this loss may result in substantial changes to physical and biological downstream processes. From a monitoring perspective, headwaters are largely ignored compared with fishable/navigable waterways for planning decisions, so their structural and functional variability is not well understood. Here, we engaged citizen scientists to contribute data on this variability and to evaluate the sensitivity of standardized cotton‐strip decomposition rates to multiscale factors across headwaters with varying landscape conditions in the Greater Toronto Area (York Region), Canada. These factors included stream, riparian vegetation, and catchment characteristics. We expected decomposition rates to be similarly sensitive to local‐ and catchment‐scale factors because of the strong links between headwater catchment and stream processes. We also expected a hump‐shaped distribution of decomposition rates across a gradient of urban cover, with stimulating effects at low to moderate cover but deleterious effects at high urban cover. We found that decomposition rate was most sensitive to local‐scale factors (e.g., strip burial, stream velocity, and both local upland riparian vegetation density and topography) rather than whole catchment properties. We did not find the expected hump‐shaped distribution with urban cover and suggest that more mechanistic studies are needed for understanding cotton‐strip decomposition to control for local factors in determining the scale at which decomposition rate is most sensitive to land cover change.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".