Comment on “Catastrophic ice shelf breakup as the source of Heinrich event icebergs” by C. L. Hulbe et al.
Bibliographic record
Abstract
[1] Hulbe et al. [2004] argue that the original binge-purge model of their coauthor MacAyeal [1993a, 1993b] is not appropriate for Heinrich events but that a new ice-shelf-collapse mechanism may work. We believe that the new collapse mechanism disagrees with important data and that MacAyeal’s original model remains viable after appropriate modifications. We have enjoyed a fascinating discussion with Hulbe et al. on this topic, which is stimulating our thinking and research, and we present some of the arguments here for a wider audience. [2] In MacAyeal’s model, thermal cycling in the Hudson Strait region of the Laurentide Ice Sheet caused an alterna-tion between long-lived ice sheet growth and short-lived ice stream draw-down. The main evidence used by Hulbe et al. [2004] against MacAyeal [1993a, 1993b] is that each
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.053 | 0.051 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".