Freshwater diatom biogeography in the Canadian Arctic Archipelago
Bibliographic record
Abstract
Abstract Aim Document the biogeographical distributions of diatoms in the Canadian Arctic in relation to environmental factors. Location The Canadian Arctic Archipelago. Methods Diatoms were extracted from lake sediments and treated using standard methods. Rarefaction‐estimated species richness, diatom concentrations (valves cc−1), and diatom abundance were mapped using a Geographic Information System. The physical and chemical parameters of the lakes were measured. Results A total of 326 taxa from 63 genera were found in 62 lakes of the study area. Up to 85 and as low as eight taxa were identified in any one lake, and rarefaction‐estimated species richness correlated with lake size. Nearby lakes could have greatly different diatom assemblages. Many taxa showed limited distributions. Response surfaces and ordination techniques indicate that a large number of taxa prefer colder regions of the Arctic while the abundance of others was influenced by lake water chemical parameters such as TKN and SiO2. Main conclusions Although many taxa were common and found across the study area, diatom assemblages showed regional differences in the Arctic. Some taxa were not found in either the northern or southern parts of the Archipelago and others were restricted to particular regions. Newly delineated genera showed interpretable geographical patterns and could be related to environmental factors, suggesting that this more natural grouping may enhance our understanding of diatom ecology. Geographical, physical, and chemical factors are needed to explain diatom distributions in the Arctic.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".