Supplementary Tables for "A 3.3-Million-Year Record of Antarctic Iceberg Rafted Debris and Ice Sheet Evolution Quantified by Machine Learning"
Bibliographic record
Abstract
Supplementary Tables for "A 3.3-Million-Year Record of Antarctic Iceberg Rafted Debris and Ice Sheet Evolution Quantified by Machine Learning" Table Captions: Table S1. Site U1537 Age Model Tie Points from Weber et al. (2022) and Reilly et al. (2021) Table S2. Site U1537 Age Model used in this study, applying both the age tie points from Weber et al. (2022) and Reilly et al. (2021) Table S3. Hole U1538A correlation to the Dove Basin Stack from Bailey et al. (2022), and the addition of the U1538 splice CCSF-A depth to the Dove Basin CCSF-A Table S4. Site U1538 splice table used in this study, note the continuation down Hole A after Core 14H Table S5. New top core section offsets for Site U1536 cores added to the Reilly et al. (2021) extended splice table Table S6. New top core section offsets for Site U1537 cores added to Reilly et al. (2021) extended splice table Table S7. Comparison of Convolutional Neural Network IRD counts to shipboard eye counts of IRD at Site U1536 Table S8. Site U1537 CNN IRD Counts per 50 cm bins Table S9. Site U1536 IRD Fluxes Per 5 kyr Quantified by a Convolutional Neural Network (0-3.3 Ma) Table S10. Site U1537 IRD Fluxes Per 5 kyr Quantified by a Convolutional Neural Network (0-3.3 Ma) Table S11. Site U1536 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma) Table S12. Site U1537 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma) Table S13. Site U1538 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.717 | 0.305 |
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".