A new approach to quantifying stratigraphical resolution: application to global stratotypes
Bibliographic record
Abstract
Horizon annealing (HA) provides a method to order all horizons in a chronostratigraphic data set, including marker beds and isotopic excursions, as well as horizons that lack exact local markers (such as taxon first appearances) and are, thus, constrained only by local stratigraphical order. Global stratotype section and point (GSSP) levels placed within an HA composite succession can be precisely correlated with levels in all other sections in the composite that span the same interval. We present two approaches to the quantitative assessment of the uncertainty or error in the placement of horizons within the composite section: a permutation method (jackknife analysis) and a sensitivity analysis (the relaxed fit curve). From jackknife analysis, we calculate a standard deviation (σ) of the variation in horizon placements and estimate the 95% confidence interval of the variation in horizon placement within the composite. We also directly assess the relative stability of event positions using the results of the jackknife. These approaches provide an objective method for assessing the relative strengths of GSSP candidates, in which we prefer those sections and horizons that are the most precisely controlled in the HA composite. Integration of biostratigraphical, chemostratigraphical and lithostratigraphical marker horizons into the HA process markedly improves levels of constraint within the composite compared to biostratigraphical data alone. In a temporally scaled composite, the 95% confidence intervals on horizon placements could be used to estimate temporal resolution. In our study, we estimate that the average temporal resolution is approximately 319 kyr, which approaches the range of that needed to test hypotheses of orbitally driven cyclicity.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".