Dissolved organic carbon load variability in the rainfall season dominated its interannual variability in the snowmelt driven basin: Evidence from 41 years data of headwater streams
Bibliographic record
Abstract
Abstract Numerous reports have observed variations of dissolved organic carbon (DOC) under the changing environment. From the DOC load variability perspective, however, how much of the variability in DOC load occurred among different seasons, and which season was most synchronized with the interannual variability remains unknown. The weekly DOC concentration and daily discharge records from four headwater streams with long‐term (1978–2018) records in the snowmelt driven basin of Harp Lake, south‐central Ontario, Canada, were used to answer these issues. We also examined the contributions of variabilities in discharge and DOC concentration to the variability in DOC load. We found that the variability of DOC load in the rainfall season (autumn) instead of snowmelt season (spring) was most closely synchronized with its interannual variability, suggesting that if the DOC variability in the rainfall season stabilized, the interannual variability would be more stable. The interannual variabilities for discharge, DOC concentration and DOC load showed an insignificant decreasing trend in the monitored 41 years, and the respective contributions of the variability trend of discharge and DOC concentration to DOC load in rainfall season increased from 28.82% to 144.58% and decreased from 71.18% to −44.58% because the change trend of discharge variability was greater than that in DOC concentration. This study provides a case information in determining which factors contribute most to the variability of DOC load in the watershed, and which season may dominate its variability throughout the year.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".