Establishing a Best Practice for SDTrimSP Simulations of Solar Wind Ion Sputtering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Solar wind (SW) ion irradiation on airless bodies can play an important role in altering their surface properties and surrounding exosphere. Much of the ion sputtering data needed for exosphere studies come from binary collision approximation sputtering models such as TRansport of Ions in Matter and its more recent extension, SDTrimSP. These models predict the yield and energy distribution of sputtered atoms, along with the depth of deposition and damage of the substrate, all as a function of the incoming ion type, impact energy, and impact angle. Within SDTrimSP there are several user-specific inputs that have been applied differently in previous SW ion sputtering simulations. These parameters can influence the simulated behavior of both the target and sputtered atoms. Here, we have conducted a sensitivity study into the SDTrimSP parameters in order to determine a best practice for simulating SW ion impacts onto planetary surfaces. We demonstrate that ion sputtering behavior is highly sensitive to several important input parameters including the ion impact angle and energy distribution and the ejected atom surface binding energy. Furthermore, different parameters can still result in similarities in the total sputtering yield, potentially masking large differences in other sputtering-induced behaviors such as the elemental yield, surface concentration, and damage production. Therefore, it is important to consider more than just the overall sputtering behavior when quantifying the importance of different parameters. This study serves to establish a more consistent methodology for simulations of SW-induced ion sputtering on bodies such as Mercury and the Moon, allowing for more accurate comparisons between studies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it