Constraining depositional models in the Barents Sea region using detrital zircon U–Pb data from Mesozoic sediments in Svalbard
Bibliographic record
Abstract
Detrital zircon U–Pb laser ablation inductively coupled plasma mass spectrometry age data on sandstones from Mesozoic successions on Svalbard are used to investigate provenance changes over time, constrain potential source areas, and to test and refine previous interpretations of the Mesozoic filling of the Barents Sea. The zircon age data indicate a western Laurentian (North Greenland) source in the Early and Middle Triassic. The westerly derived sediments most likely include reworked older sediments with proto-sources in Canada and Greenland. Sediments reaching Svalbard in the Late Triassic display a distinct Uralide signature that demonstrates derivation from the east. Zircon age populations in Late Triassic–Early Jurassic sands suggest mixing of zircons from the Early and Middle Triassic and Late Triassic sediments; the data are interpreted to reflect reworking of older Mesozoic sands and possible renewed input of sediments from the west. The data thus demonstrate a shift from westerly to easterly sediment sources in the early Late Triassic. The Early and Middle Triassic zircon age signature in this study appears to resurface in published Early Cretaceous provenance data from Svalbard, suggesting that sediment input from the east ceased during the Jurassic, and shifted back to westerly sources.
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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 | 0.000 |
| 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) | 0.000 | 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".