Marine and limnic radiocarbon reservoir corrections for studies of late- and postglacial environments in Georgia Basin and Puget Lowland, British Columbia, Canada and Washington, USA
Bibliographic record
Abstract
Models of late-glacial environmental change in coastal areas are commonly based on radiocarbon ages on marine shell and basal lake sediments, both of which may be compromised by reservoir effects. The magnitude of the oceanic reservoir age in the inland waters of the Georgia Basin and Puget Lowland of northwestern North America is inferred from radiocarbon ages on shell-wood pairs in Saanich Inlet and previously published estimates. The weighted mean oceanic reservoir correction in the early and mid Holocene is −720±90 yr, slightly smaller than, but not significantly different from, the modern value. The correction in late-glacial time is −950±50 yr. Valley-head sites yield higher reservoir values (−1200±130 yr) immediately after deglaciation. The magnitude of the gyttja reservoir effect is inferred from pairs of bulk gyttja and plant macrofossil ages from four lakes in the region. Incorporation of old carbon into basal gyttja yields ages from bulk samples that are initially about 600 yr too old. The reservoir age declines to less than 100 yr after the first millennium of lake development. When these corrections are accounted for, dates of deglaciation and late-glacial sea-level change in the study area are pushed forward in time by more than 500 yr.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".