Bibliographic record
Abstract
Abstract. Radiocarbon may serve as a powerful dating tool in palaeoceanography, but its accuracy is severely limited by the need to calibrate radiocarbon dates to calendar ages. A key problem is that marine radiocarbon dates must be corrected for past offsets from either the contemporary atmosphere (i.e. ‘reservoir age’ offsets) or a modelled estimate of the global average surface ocean (i.e. delta-R offsets). This presents a challenge because the spatial distribution of reservoir ages and delta-R offsets can vary significantly, particularly over periods of major marine hydrographic and/or carbon cycle change such as the last deglaciation. Modern reservoir age/delta-R estimates therefore have limited applicability. The construction of regional marine calibration curves could provide a solution to this challenge. However, the spatial reach of such calibrations, and their robustness subject to temporal changes in climate and ocean circulation would need to be tested. Here, we use unsupervised machine learning techniques to define distinct regions of the surface ocean that exhibit coherent behaviour in terms of their radiocarbon age offsets from the contemporary atmosphere (R-ages). We investigate the performance of multiple algorithms (K-Means, K-Medoids, hierarchical clustering) applied to outputs from 2 different numerical models, spanning a range of climate states and timescales of adjustment. Comparisons between the cluster assignments across model runs confirm some robust regional patterns that likely stem from constraints imposed by large-scale ocean and atmospheric physics (i.e. locations of deep mixing, gyres, fronts, divergence etc.). At the coarsest scale, regions of coherent R-age variability correspond to the major ocean basins (Arctic, Atlantic, Southern, Indo-Pacific). By further dividing basin-scale shape-based clusters into amplitude-based subclusters, we recover regional associations that cohere with known modern oceanographic processes, such as increased high latitude R-ages, or the propagation of R-age anomalies from regions of deep mixing in the Southern Ocean to upwelling sites in the Eastern Equatorial Pacific. We show that the medoids (i.e. the most representative locations) for these regional sub-clusters provide significantly better approximations of simulated local R-age variability than constant offsets from the global surface average. This is found to hold when cluster assignments obtained from one model are applied to simulated R-age time series from another. The proposed clusters are also found to be broadly consistent with existing reservoir age reconstructions that span the last ~30 ka. We therefore propose that machine learning provides a promising approach to the problem of defining regions for which marine radiocarbon calibration curves may eventually be generated.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.283 | 0.206 |
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".