Cumulative probability functions and their role in evaluating the chronology of geomorphological events during the Holocene
Bibliographic record
Abstract
Cumulative probability functions (CPFs) for large numbers of radiocarbon age determinations are increasingly being used by scientists as a methodology to discern environmental histories. While the recent compilation of regional databases of the radiocarbon dating control for fluvial sediment sequences has been beneficial for identifying gaps in knowledge and stimulating new research, there are a number of problems that critically undermine the use of these CPFs as sensitive hydroclimatic proxies. (i) The CPF method is underpinned by the assumption that each radiocarbon measurement is a true age estimate for a point in time, whereas each measured age in fact forms a scatter around the true age of the sample; (ii) calibration of radiocarbon ages is responsible for much of the structure in CPFs and compounds the problem of scatter and smears the chronological control; (iii) the databases incorporate multiple types of environmental changes differing chronological relationships between the 14 C measurements and the dated events, with pre-dating, dating or post-dating chronological control each displaying variable length temporal lags all mixed together in the same analysis; and (iv) the radiocarbon ages from individual case studies need to be more robustly tested before being incorporated into regional databases. All these factors negate the value of CPFs as sensitive proxies of environmental change, because peaks in probability for individual radiocarbon measurements are likely to be an incorrect estimate for the age of a geomorphological event and this problem is compounded by combining probabilities for multiple unrelated events. In this paper we present a critical analysis of CPFs and their interpretation before suggesting alternative approaches to analysing radiocarbon geochronologies of geomorphic events, which include: (i) Bayesian age modelling of river terrace development; (ii) developing regional databases that test specific geomorphic hypotheses; (iii) Bayesian age modelling of palaeoflood records; and (iv) analysis of sedimentation rates. Copyright # 2011 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".