A high-resolution paleolimnological study of climate and human impacts on Lac Noir, Québec, over the past 1000 yr
Bibliographic record
Abstract
Abstract Diatom assemblages in lake sediments of Lac Noir, southwestern Québec, were studied at a resolution of 10 yr to determine principal drivers of primary producers for the past ~1000 yr. Generalized additive modeling revealed strong links between broadscale climate intervals of the late Holocene, forest composition, and diatom flora. During the Medieval Warm Period (~AD 1200) and onset of the Little Ice Age (~AD 1450), increases in Tabellaria flocculosa str. IIIp at the expense of Discostella stelligera reflected low lake productivity. At AD 1630, an abrupt shift to cooler temperatures and dry conditions triggered a decline in hemlock (Tsuga), replaced by disturbance and cool-adapted taxa. Greater nutrient availability and soil erosion in the catchment led to a corresponding and rapid increase in diatoms with higher optima for nitrogen, such as Asterionella formosa and Fragilaria crotonensis. After AD 1870, an increase in pollen of taxa associated with disturbances signaled the arrival of Euro-Canadians, and associated nutrient inputs to the lake triggered increases in Stephanodiscus minutulus and Achnanthidium minutissimum. Overall results of the study indicate that climate played an important underlying role in lake-ecosystem dynamics; however, disturbances affecting forest composition had more direct influences on the diatom communities of Lac Noir.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".