Why do we need (another) universal tracers portal in Astrobiology?
Bibliographic record
Abstract
Abstract Astrobiology is a scientific field that is very interdisciplinary and developing very fast, with many new discoveries generating a high level of attention in both the scientific community and the public. A central goal of astrobiology is to discover life beyond Earth which is, with our current instrumentation and knowledge, arguably within our reach. However, knowledge exchange crossing disciplinary boundaries is becoming increasingly challenging due to different usage of nomenclature and scientific controversies often limited to subdisciplines. There have been some efforts to compile organized databases of terms, concepts and other relevant material within some of the subfields contributing to astrobiology, for example through manually curated online portals designed to benefit students, teachers and practitioners of astrobiology-related research. However, the developments within the subfields and the potentially premature communication of research findings are too fast for objective research portals to remain reliable and up-to-date enough to enable well-informed scientific discussions. We suggest here a novel strategy for developing an online tracers portal as a self-maintaining and self-updating information platform, that would allow not only for a relatively unbiased selection of research results, but also provide fast access to latest scientific discoveries together with potential controversies, such that users of the tracers portal can form their own opinion on all available data rather than obtaining an already filtered and potentially biased selection of information.
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 | 0.058 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.021 | 0.060 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.021 |
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".