Dust Destruction in Nonradiative Shocks
Bibliographic record
Abstract
Abstract Supernova remnant (SNR) shock waves are the main place where interstellar dust grains are destroyed. However, the dust destruction efficiency in nonradiative shocks is still not well known. One way to estimate the fraction of dust destroyed is to compare the difference between postshock gas abundances and preshock medium total abundances when the preshock elemental depletion factors are known. We compare the postshock gas abundances of 16 SNRs in the Large Magellanic Cloud (LMC) with the LMC interstellar medium abundances that we derived based on 69 slow-rotating early B-type stars. We find that, on average, ∼61% of Si-rich dust grains are destroyed in the shock, while the fraction of dust destroyed is only ∼40% for Fe-rich dust grains. This result supports the idea that the high depletion of Fe in the diffuse neutral medium is not caused by the resilience of Fe-rich grains but because of faster growth rate. This work also presents a potential way to constrain the chemical composition of interstellar dust.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".