Supplementary Tables for "A 3.3-Million-Year Record of Antarctic Iceberg Rafted Debris and Ice Sheet Evolution Quantified by Machine Learning"
<p>Supplementary Tables for "A 3.3-Million-Year Record of Antarctic Iceberg Rafted Debris and Ice Sheet Evolution Quantified by Machine Learning"</p> <p> </p> <p><strong>Table Captions:</strong></p> <p><strong>Table S1.</strong> Site U1537 Age Model Tie Points from Weber et al. (2022) and Reilly et al. (2021)</p> <p><strong>Table S2. </strong>Site U1537 Age Model used in this study, applying both the age tie points from Weber et al. (2022) and Reilly et al. (2021)</p> <p><strong>Table S3. </strong>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</p> <p><strong>Table S4. </strong>Site U1538 splice table used in this study, note the continuation down Hole A after Core 14H</p> <p><strong>Table S5. </strong>New top core section offsets for Site U1536 cores added to the Reilly et al. (2021) extended splice table</p> <p><strong>Table S6. </strong>New top core section offsets for Site U1537 cores added to Reilly et al. (2021) extended splice table</p> <p><strong>Table S7. </strong>Comparison of Convolutional Neural Network IRD counts to shipboard eye counts of IRD at Site U1536</p> <p><strong>Table S8. </strong>Site U1537 CNN IRD Counts per 50 cm bins</p> <p><strong>Table S9. </strong>Site U1536 IRD Fluxes Per 5 kyr Quantified by a Convolutional Neural Network (0-3.3 Ma)</p> <p><strong>Table S10. </strong>Site U1537 IRD Fluxes Per 5 kyr Quantified by a Convolutional Neural Network (0-3.3 Ma)</p> <p><strong>Table S11. </strong>Site U1536 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p> <p><strong>Table S12. </strong>Site U1537 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p> <p><strong>Table S13. </strong>Site U1538 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4