Not functional yet a difference maker: junk DNA as a case study
Bibliographic record
Abstract
Abstract It is often thought that non-junk or coding DNA is more significant than other cellular elements, including so-called junk DNA. This is for two main reasons: (1) because coding DNA is often targeted by historical or current selection, it is consideredfunctionallyspecial and (2) because its mode of action is uniquely specific amongst the other actual difference makers in the cell, it is consideredcausallyspecial. Here, we challenge both these presumptions. With respect to function, we argue that there is previously unappreciated reason to think that junk DNA is significant, since it can alter the cellular environment, and those alterations can influence how organism-level selection operates. With respect to causality, we argue that there is again reason to think that junk DNA is significant, since it too (like coding DNA) is remarkably causally specific (in Waters’, in J Philos 104:551–579, 2007 sense). As a result, something is missing from the received view of significance in molecular biology—a view which emphasizes specificity and neglects something we term ‘reach’. With the special case of junk DNA in mind, we explore how to model and understand the causal specificity, reach, and correspondingefficacyof difference makers in biology. The account contains implications for how evolution shapes the genome, as well as advances our understanding of multi-level selection.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".