Temporal organic matter dynamics in anthropogenically-impacted stream ecosystems
Bibliographic record
Abstract
Stream ecosystems are heavily influenced by the landscapes that surround them. The input of organic matter from terrestrial and aquatic sources sustains stream ecosystems by providing a base energy source for food webs. When landscapes are changed by human activities (e.g., urbanization, agricultural intensification), critical ecosystem functions such as nutrient processing are altered, and the composition of nutrients available. Explorations into these processes provide valuable insights into trophic energy flows and carbon cycling in streams, particularly in the face of climate change. This study seeks to advance knowledge on the ecosystem ecology of human-impacted streams by addressing a critical knowledge gap on organic matter processing rates and carbon dynamics in streams across an anthropogenic disturbance gradient. Situated in the Windsor-Essex region of southwestern Ontario, Canada, the stream ecosystems in this study (n=7) are impacted from both urbanized and agricultural land uses. Using standardized cotton-strip assays to measure carbon processing rates (i.e., decomposition) and fluorometric methods to characterize molecular carbon structure, I characterized monthly shifts to test whether anthropogenic sites behave in a similar way. Results from this work have generated a novel baseline dataset that encompasses temporal drivers of decomposition rates along with molecular carbon characterization across a land-use gradient. In temperate regions such as the Laurentian Great Lakes basin, human impacts on freshwater ecosystem processes are complex and representative of ecosystems globally. Disentangling drivers of variability on ecosystem functions in contemporary contexts is therefore critical to advance understanding of ecosystem ecology on local and global scales and inform effective management and restoration.
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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 | 0.001 | 0.001 |
| 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".