ESTIMATING MARINE RESERVOIR EFFECTS IN ARCHAEOLOGICAL CHRONOLOGIES: COMPARING ΔR CALCULATIONS IN PRINCE RUPERT HARBOUR, BRITISH COLUMBIA, CANADA
Bibliographic record
Abstract
The best method for quantifying the marine reservoir effect (MRE) using the global IntCal Marine13 calibration curve remains unresolved. Archaeologists frequently quantify uncertainty on MRE values as errors computed from single pairs of marine-terrestrial radiocarbon ages, which we argue significantly overstates their accuracy and precision. Here, we review the assumptions, methods, and applications of estimating MRE via an estimate of the additional regional offset between the marine and terrestrial calibration curves (ΔR) for the Prince Rupert Harbour (PRH) region of British Columbia, Canada. We acknowledge the influence on ΔR of MRE variation as (1) a dynamic oceanographic process, (2) its variable expression in biochemical and geochemical pathways, and (3) compounding errors in sample selection, measurement, and calculation. We examine a large set of marine-terrestrial pairs (n= 63) from PRH to compare a common archaeological practice of estimating uncertainty from means that generate an uncertainty value of ±49 years with a revised, more appropriate estimate of error of ± 230 years. However, we argue that the use of multiple-pair samples estimates the PRH ΔR as 273 ± 38 years for the last 5,000 years. Calculations of error that do not consider these issues may generate inaccurate age estimates with unjustifiable precision.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".