Examining the impact of stream pH changes on transient P storage in streambed sediments across multiple temporal scales
Bibliographic record
Abstract
Canadian Laurentian Great Lakes Basin. Study examines the potential for stream pH changes to modulate the storage of phosphorus (P) in streambed sediments across multiple temporal scales by (i) synthesizing experimental data that quantify the relationships between pH and sediment P exchange, and (ii) comparing these relationships to measured long-term, seasonal, diel and event-based pH changes observed in streams. Synthesis of experimental data indicate that P exchange is highly sensitive to pH changes over the pH range typically observed in Ontario streams (pH >7). Of 157 monitored streams, 84 % experienced significant pH increases over the last 35 years, averaging + 0.24 pH change, and favouring long-term P release from streambed sediments or limiting P retention. Seasonally, most streams exhibited higher pH during summer, suggesting P release from sediments at times of high-productivity. Over shorter timescales, a subset of streams demonstrated diel pH fluctuations of > 1 pH unit in summer, and rapid pH changes (up to 1.7 pH units) during precipitation and snowmelt events. pH changes of this magnitude may drive rapid and extensive P exchange from streambed sediment. Overall, our results indicate pH changes may be an under-appreciated control on the transient P storage within streams, with relevance at multiple temporal scales. This finding has implications for P load predictions by mechanistic watershed nutrient models. • pH is an under-appreciated driver of transient P storage in streams. • P exchange highly sensitive to pH over pH range typically observed in streams. • Long-term pH increase in Ontario streams favours P release from sediments. • Seasonal, diel and event-based pH changes expected to drive P exchange.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".