Is There Life on Mars? Studying the Context of Uncertainty in Astrobiology
Bibliographic record
Abstract
While science is often portrayed as producing reliable knowledge, scientists tend to express caution about their claims, acknowledging nuances and doubt, all the more so in novel domains of research paved with unknowns. Uncertainty is an intrinsic aspect of scientific inquiry, particularly in recent fields such as astrobiology, which tackles numerous hard questions about the origin, evolution, and distribution of life on Earth and elsewhere. Mapping uncertainty in science matters for achieving a more accurate understanding of scientific knowledge. It also helps identify research domains at the frontiers of knowledge where unknowns are the most salient. In this article, we investigate the presence, distribution and context of uncertainty in the field of astrobiology. We analyze a comprehensive corpus of 3,698 research articles published in three major journals in the domain from 1968 to 2020. We use a linguistically motivated approach to identify expression of uncertainty in article full text. The corpus was further segmented into research topics using Latent Dirichlet Allocation (LDA) to investigate variations in uncertainty across subfields and over time. Our findings show that, while uncertainty has remained relatively stable over the 50 years covered by the corpus, constituting 20–25% of sentences on average, it varies significantly across research fields, highlighting areas where unknowns, doubts and speculations are more prevalent. The analysis also highlights relationships between expression of uncertainty and rhetorical structure. Indeed, higher uncertainty levels were observed in the beginning (introductions) and towards the end (conclusions) of research articles, while middle sections contained less uncertainty. Abstracts also tended to express a slightly higher level of uncertainty compared to main texts, especially with greater variability, suggesting their role in summarizing research and highlighting unknowns. To investigate the context of uncertainty, a lexical analysis was conducted to identify nouns most frequently associated with uncertainty within each topic. Terms such as “life,” “planet,” and “Mars” were found to be strongly associated with uncertainty. Conversely, terms related to experimentation and measurement, such as “sample” and “spectrum,” were linked to an absence of uncertainty, pointing at a dichotomy between speculative and evidence-based lines of inquiry. The findings contribute to a better understanding of the field of astrobiology and exemplify the relevance of the proposed method to identify uncertaintyrelated concepts in corpora of full text publications. They also offer a foundation for future comparative studies across disciplines.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".