Dynamics and chemistry of dissolved organic carbon in Precambrian Shield catchments and an impounded wetland
Bibliographic record
Abstract
We examined the sources, sinks, and fluxes of dissolved organic carbon (DOC) and its chemical fractions over a 4-year period in upland and wetland catchments in the Experimental Lakes Area, Ontario. In an upland catchment, the major sources of DOC were precipitation, vegetation, and soil organic matter, resulting in a summer (June to October) export of 2.1 g·m2. DOC concentrations in peat pore water ranged from 20 to 60 mg·L1and were related to hydrologic pathways. DOC export from catchments containing wetlands ranged from 0.8 to 5.7 g·m2and export from the peatland portion of the catchment was estimated to be 2830 g·m2. Flooding to a depth of 1.2 m in the peatland section of one catchment resulted in an increase in pore-water and pond DOC concentrations, attributed to the decomposition of plant tissues and peat. Because only 14% of the catchment was flooded, impoundment did not result in a significant increase in DOC export. Between 85 and 93% of DOC was contained in the acid fractions, with small amounts in the neutral fractions. Flooding increased the proportion of the hydrophilic neutral fraction in peat pore waters for 2 years after flooding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".