Paleoclimate studies of minerogenic sediments using annually resolved textural parameters
Bibliographic record
Abstract
We obtained quantitative multivariate data from each varve of a minerogenic lacustrine sequence from the Canadian High Arctic, using an image analysis technique applied to thin‐sections. The information on each varve from the uppermost core section was compared with a 35 yr meteorological dataset. Snowmelt intensity, which is an index reflecting the energy available for sediment transport, correlates well with the median grain‐size measured for each varve, as well as with the weight of the 10–20 and 20–60 μm fractions. The proportion of fine silt also correlates with low intensity summer precipitation. This methodology allows us to decipher the climatic control on sedimentary processes and yield a new perspective for constructing models which link climate to sediments that contain few biological remains. This model can then be used to infer paleoclimate with annual resolution from the downcore grain‐size analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".