Paleoenvironmental inference models from sediment diatom assemblages in Baffin Island lakes (Nunavut, Canada) and reconstruction of summer water temperature
Bibliographic record
Abstract
Diatoms from the surface sediments of 61 lakes on Baffin Island, Nunavut, were identified, enumerated, and interpreted quantitatively. The samples span a latitudinal transect from 62 to 74°N, reflecting climatic and vegetational gradients that range from low Arctic to transitional mid-Arctic to high Arctic. While the sampled lakes encompass both predominantly maritime and continental climatic regimes, sites have been deliberately restricted to basins in Precambrian crystalline terrain to mitigate the edaphic consequences of alkaline sedimentary bedrock. Canonical correspondence analysis, using forward selection and Monte Carlo permutation tests, identified pH, conductivity, summer lake water temperature, and mean annual air temperature as significant environmental controls over diatom assemblages. Using weighted-averaging regression and calibration, predictive models for these parameters have been developed. When applied to down-core assemblages, the summer lake water temperature model provides realistic reconstructions when compared with other paleoenvironmental records. Over the past 5000 years, the amplitude of reconstructed summer lake water temperature is on the order of 4.0°C, expressed primarily as progressive Neoglacial cooling culminating in the Little Ice Age. Diatom-inferred summer water temperatures have increased by 2.0°C in the past 150 years, again in agreement with independent paleoclimatic reconstructions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".