Peatland-Stream Hydrological and Biogeochemical Connectivity in the James Bay Lowland, Ontario
Bibliographic record
Abstract
The Hudson-James Bay Lowlands are the second largest peatland dominated area on the planet, and are expected to be particularly vulnerable to future climate change. Changes in climate will affect peatland hydrology and biogeochemistry, impacting the aquatic ecosystems this region supports, however there is limited information about the hydrology and biogeochemistry of this landscape under current conditions. This thesis focuses on assessing the nature of hydrological and biogeochemical connectivity between a fen and 2nd order channel in the Central James Bay Lowland, Ontario. Specifically the study focuses on the role of preferential hydrological flowpaths in the riparian area, such as soil pipes and rivulets. We used water table-discharge relationships to examine the nature of hydrological connectivity between the fen and riparian area and identified thresholds of hydrological connectivity using these relationships. Once the storage thresholds in the near stream depression and fen have been met, peak flow can be generated in the soil pipes and rivulets, this occurs under wet antecedent conditions late in the fall. The study also identified that the riparian area is a likely dominant source of DOC and MeHg despite the extensive peatlands that dominate the upslope region, and that this area has a unique chemical signature from the fen. Furthermore late fall storm events with wet antecedent conditions were found to play an important role in solute transport from the soil pipes, with as much as >60% of the total solute load for one soil pipe occurring during a storm event which had a duration of only 17% of the monitoring period.
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 | 0.001 |
| 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) | 0.002 | 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".