Identification of coherent links between interannual sedimentary structures and daily meteorological observations in Arctic proglacial lacustrine varves: potentials and limitations
Bibliographic record
Abstract
Proglacial lacustrine sediments from High Arctic Lake R (76°17.9′N, 90°59.3′W, unofficial name) are shown to be annually laminated (varved) and contain a variety of subannual structures. The formation of the subannual structures (and overall varve) was controlled by a combination of meteorologic (temperature and rainfall) and geomorphic factors. Using a training set of the ten thickest varves in the 38-year sedimentary record, a heuristic model was developed to link subannual structures with regional meteorological conditions. Within the training set, significant correlations were shown between subannual structure thickness and the magnitude of the corresponding melt event, defined as a period of continuously positive temperature. However, these correlations deteriorated as the varves progressively thinned, and several varves exhibited no relationship between their subannual structures and respective meteorological conditions. Grain size analyses showed that the thin varves were significantly finer than the thick varves and are inferred to reflect changed sediment inflow patterns that altered deposition and reduced the fidelity of the model. Despite these complexities, this study identified the potential to produce long-term, subannual reconstructions of weather conditions. Model results revealed the limitations of simple varve–meteorology relationships, as well as identified necessary environmental and sampling conditions required to produce a more robust model for future applications.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".