Bibliographic record
Abstract
Today more than ever, space science is a vibrant and exciting field. The Mars 2020 Perseverance Rover took off and landed at the height of the Covid-19 pandemic, and the scientific results of its payload are already paying dividends to the scientific community. Meanwhile on Earth, scientific development, far from having halted, remains as active as ever before, albeit with some hiccups over the last 3 years due to restrictions. Nevertheless, the scientific community, and more specifically, the space science community, has remained steadfast in its pursuit of knowledge. And at the core of this pursuit is the ever-growing field of chemometrics. All in all, the body of work in this special issue represents a tremendous effort on the part of the authors, and we could not be more pleased. We must admit that the continued submissions and forthcoming work made it hard for us to declare a conclusion to this special issue. Indeed, we could have continued receiving submissions indefinitely. However, all good things must come to an end, if for no other reason than to open the door for future endeavors. Whether that means the continuation of methodological work, the inevitable continuation of instrument development for the search of life or other priorities of the space science communities, or simply reflections on where we are headed, there is much to be done and disseminated. But as long as we continue having fun and pushing the proverbial envelope, special issues such as this one should be executed every few years to ensure that the fields of space science and chemometrics benefit from the synergy of our long-standing interdisciplinarity. For now, enjoy the ride and shoot for the stars, if only to land on the Moon or Mars!
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.230 | 0.136 |
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".