Diatom assemblages and limnological variables from 40 lakes and ponds on Bathurst Island and neighboring high Arctic islands
Bibliographic record
Abstract
Abstract We examined the influence of catchment geology, specifically differences in buffering capacity, on the limnological characteristics and surface sediment diatom assemblages from lakes and ponds from Bathurst Island, High Arctic Canada. Differences in buffering capacity exist on Bathurst Island due to a geological gradient that spans from carbonate‐bearing limestone in the east, to more stable quartz sandstone, siltstone, and shale in the west. We collected physical and chemical limnological data, as well as surface sediment diatom assemblages from nine ponds on the poorly buffered western portion of the island and combined these observations with a previously published dataset of 31 lakes and ponds, from the well‐buffered eastern region. The addition of these nine ponds expanded the pH gradient of the existing Bathurst Island dataset (pH 8.0–8.6) to pH 6.8–8.6. A regional, weighted average diatom‐inferred pH model was developed and showed strength similar to other Arctic calibration sets ( , root‐mean‐squared‐error of prediction (RMSEP) = 0.298). Given the links between climate and pH shifts in the High Arctic, the ability to reconstruct pH should be a valuable tool for future paleolimnological studies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".