Bibliographic record
Abstract
The binary nature of life is deeply ingrained in daily experiences, evident in the stark distinctions between life and death and the living and the inert. While this binary perspective aligns with disciplines like medicine and much of biology, uncertainties emerge in fields such as microbiology, virology, synthetic biology, and systems chemistry, where intermediate entities challenge straightforward classification as living or non-living. This contribution explores the motivations behind both binary and non-binary conceptualizations of life. Despite the perceived necessity to unequivocally define life, especially in the context of origin of life research and astrobiology, mounting evidence indicates a gray area between what is intuitively clearly alive and what is distinctly not alive. This prompts consideration of a gradualist perspective, depicting life as a spectrum with varying degrees of "lifeness". Given the current state of science, the existence or not of a definite threshold remains open. Nevertheless, shifts in epistemic granularity and epistemic perspective influence the framing of the question, and scientific advancements narrow down possible answers: if a threshold exists, it can only be at a finer level than what is intuitively taken as living or non-living. This underscores the need for a more refined distinction between the inanimate and the living.
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) | 0.008 | 0.002 |
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; both teacher heads agree on what is shown here.
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".