Ecological characteristics of modern diatom assemblages from Axel Heiberg Island (High Arctic Canada) and their application to paleolimnological inference models
Bibliographic record
Abstract
Relationships between modern surface sediment diatom assemblages and measured water chemistry variables were examined from 30 lakes and ponds on Axel Heiberg Island (Nunavut) in the Canadian High Arctic. Canonical correspondence analysis with forward selection and Monte Carlo permutation tests identified dissolved organic carbon, dissolved inorganic carbon, specific conductance, and pH as the measured environmental variables explaining significant proportions of the diatom variance. Canonical correspondence analysis axis 1 represented a gradient of specific conductance, and axis 2 was influenced primarily by pH. To increase the signal-to-noise ratio in our reconstructions, the species data sets were refined to include only taxa that had significant responses to either conductivity or pH, as determined by Huisman Olff Fresco models of species–environment relationships. Diatom-based inference models were subsequently developed for both lakewater specific conductance (r2boot= 0.75, root mean square error of prediction = 0.22) and lakewater pH (r2boot= 0.31, root mean square error of prediction = 0.57) using weighted averaging techniques. These data contribute to our understanding of diatom biogeography throughout the Canadian Arctic and have implications for regional paleoclimatic reconstructions in these climatically and environmentally sensitive regions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".