Hydrology drives chemical synchronicity in subarctic tundra ponds
Bibliographic record
Abstract
Abstract. Climate change has implications for the capacity of the many small shallow ponds found at high latitudes to support organisms and store carbon, which in turn has important feedbacks for climate change. As carbon cycling in ponds is linked to nutrient supply, an improved understanding of pond biogeochemistry is needed. Due to logistical challenges, many studies rely on data sets collected during a single campaign that may not be representative of the entire ice-free season. This study characterized spatial and temporal patterns in water chemistry in tundra ponds to determine: (1) if temporal variability in pond chemistry exceeded spatial variability; (2) if temporal variability existed, whether all ponds (or groups of ponds) behaved in a similar temporal pattern, linked to season or hydrology; (3) if spatiotemporal variability in pond biogeochemical signatures could be used to make inferences about processes occurring within ponds and between ponds and surrounding peatlands. Six shallow ponds located in the Hudson Bay Lowlands region were monitored ~ biweekly throughout the ice-free season (May 1–October 31, 2015) for concentrations of nitrogen species and major ions. Temporal variability exceeded spatial variability (variation among ponds) in pond concentrations of most major ions and dissolved organic nitrogen, which appeared to be driven by the evapoconcentration and dilution of pond water. In contrast, the dissolved inorganic nitrogen species were not directly related to pond hydrologic conditions and were instead likely mediated by biological processes within ponds. This work provides an improved understanding of the relative controls of internal and external drivers of pond biogeochemical patterns, and highlights the importance of the highly variable seasonal hydrology. This work also provides insight for future field sampling campaigns.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".