<b>Macroecological and macroevolutionary patterns emerge in the universe of GNU/Linux operating systems</b>
Bibliographic record
Abstract
What leads to classically recognized patterns of biodiversity remains an open and contested question. It remains unknown if observed patterns are generated by biological or non‐biological mechanisms, or if we should expect the patterns to emerge in non‐biological systems. Here, we employ analogies between GNU/Linux operating systems (distros), a non‐biological system, and biodiversity, and we look for a number of well‐established ecological and evolutionary patterns in the Linux universe. We demonstrate that patterns of the Linux universe generally match macroecological patterns. Particularly, Linux distro commonness and rarity follow a skewed distribution with a clear excess of rare distros, we observed a power law mean‐variance scaling of temporal fluctuation, but there is only a weak relationship between niche breadth (number of software packages) and commonness. The diversity in the Linux universe also follows general macroevolutionary patterns: the number of phylogenetic lineages increases linearly through time, with clear per‐species diversification and extinction slowdowns, something that has been indirectly estimated, but not directly observed in biology. Moreover, the composition of functional traits (software packages) exhibits significant phylogenetic signal. The emergence of macroecological patterns across Linux suggests that the patterns are produced independently of system identity, which points to the possibility of non‐biological drivers of fundamental biodiversity patterns. At the same time, our study provides a step towards using Linux as a model system for exploring macroecological and macroevolutionary patterns.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".