High-frequency monitoring of dissolved organic carbon exports, stream discharge and water table depth in a peatland-dominated boreal catchment, Minganie, Quebec, Canada
Bibliographic record
Abstract
The data set present high-frequency data (1h-step) collected at the outlet of a 3 km length stream which flows through a boreal headwater catchment of 2.22 kilometer square and covered at 76% by an ombrotrophic dome-shaped peatland. The data set covered a period from June 2018 to May 2020. The measured parameters included physico-chemical parameters measured by an EXO2 multiparameter probe (YSI, USA) installed in the stream, at the outlet of the catchment. This multiparameter probe measured among other parameters (e.g., water temperature, pH, specific conductivity and dissolved oxygen saturation) the fluorescent dissolved organic matter (fDOM) which was used as a proxy of dissolved organic carbon (DOC) and a calibration was performed in order to determined the relashionship f(fDOM) = DOC. A hidden markov model was applied to this data set to decompose times series into low flow and high flow conditions. This was used to calculate specific DOC exports during those periods. Specific flood periods were isolated and specific indices were calculated for those periods. In addition, the stream discharge was mesured at the same site using a ultrasonic distance sensor (SR50, Campbell, USA) [from June 2018 to May 2019] and a water-level logger (U201-04, Hobo, Onset, USA) [from June 2019 to May 2020]. In the peatland area, the water table depth and the porewater temperature were measured hourly using a U20-001-04 water level logger (Hobo, Onset, USA) [2018] and a a U20l-04 water level logger (Hobo, Onset, USA) [2019] and the precipitation and air temperature were measured using a a tilting bucket rain gauge (Onset, 0.2 mm).
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".