Trends in Ostracoda and Cladocera distribution and water chemistry in subarctic Canada: Churchill (Manitoba) lakes and ponds revisited
Bibliographic record
Abstract
Ponds and lakes distributed across northern treeline in the Hudson Bay Lowlands near Churchill (Manitoba) were revisited to analyse and document the local ecoclimatic and limnological changes that occurred over the period 1997-2012. Our analyses revealed that single events may cause significant changes in salinity, pH and silicate content because of the limited buffer capacity of the inter-connected waters. Planktic freshwater microcrustaceans (Cladocera) presented less diverse assemblages and appeared to favour waters that are situated in the boreal forest, while the diversity of benthic species assemblages (Cladocera and Ostracoda) was highest in waters located closer to the coastline and in open tundra vegetation. We identified three species that are distinctive for the boreal ecozone (i.e., Candona acuta, Can. acutula and Can. decora) and two species (i.e., Tonnacypris glacialis and Can. rawsoni) that are elements of (sub-)arctic landscapes and potentially endangered as the northern treeline expands due to rapid warming. These species are thought to be useful indicators for future ecosystem quality assessments and/or ecosystem service management programs. Our findings were compared to other studies completed in the boreal Yukon Territory and revealed that species diversity is closely linked to landscape history.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".