Comparison of Freshwater Diatom Assemblages from a High Arctic Oasis to Nearby Polar Desert Sites and Their Application to Environmental Inference Models
Bibliographic record
Abstract
Arctic oases are regions of atypical warmth and relatively high biological production and diversity. They are small in area (<5 km(2) ) and uncommon in occurrence, yet they are relatively well studied due to the abundance of plant and animal life contained within them. A notable exception is the lack of research on freshwater ecosystems within polar oases. Here, we aim to increase our understanding of freshwater diatom ecology in polar oases. Diatoms were identified and enumerated from modern sediments collected in 23 lakes and ponds contained within the Lake Hazen oasis on Ellesmere Island, and compared with diatom assemblages from 29 sites located outside of the oasis across the northern portion of the island. There were significant differences in water chemistry variables between oasis and northern sites, with oasis sites having higher conductivity and greater concentrations of nutrients and related variables such as dissolved organic carbon (DOC). Taxa across all sites were typical of those recorded in Arctic freshwaters, with species from the genera Achnanthes sensu lato, Fragilaria sensu lato, and Nitzschia dominating the assemblages. A correspondence analysis (CA) ordination showed that oasis sites generally plotted separately from the northern sites, although the sites also appear to plot separately based on whether they were lakes or ponds. Canonical correspondence analysis (CCA) identified specific conductivity, DOC, and SiO2 as explaining significant (P < 0.05) and additional amounts of variation in the diatom data set. The most robust diatom-based inference model was generated for DOC, which will provide useful reconstructions on long-term changes in paleo-optics of high Arctic lakes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".