Climate changes along the Labrador coasts during the Holocene based from pollen assemblages
Bibliographic record
Abstract
The rapid ongoing warming recorded across northern regions is unprecedented. This warming is however not uniform across the territory and large regional discrepancies exist. It is therefore relevant to document the variations of climate in the past in both time and space in order to understand the regional climate dynamics. However, in Labrador, instrumental and historical data are rare and only cover a short period of time. Our knowledge of the natural evolution of the climate is therefore limited, which hampers our capacity to evaluate the natural modes of variability and simulate changes at regional scales. From this viewpoint, quantitative climate reconstructions from pollen assemblages are useful because they allow the development of time series covering long periods of time. Here, we report on pollen data from peat and lake sediments collected in the area of Okak, Nain and Dog Island along the Labrador coast. These data are used for climate reconstruction over the last millennia, thus allowing to document natural climate variability at regional scale. The climate parameters we reconstruct by the means of the modern analogue technique include the summer temperature, sunshine and precipitation. The results provide new insights about the climate of Labrador at local to regional scale, illustrating notably the importance of the Labrador Current on climatic conditions at nearshore locations. In fact, our climate reconstructions demonstrate a disparity with the regional climate curve which may testify of the east-west climatic gradient between islands and the land.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".