Study on DNA Binary Programming is Key to Understanding Evolutionary Biology
Bibliographic record
Abstract
Each DNA molecule consists of a base pair of nucleotides, either guanine (G) coupled with cytosine (C), or adenine (A) coupled with thymine (T). GC and AT base molecules linked together in long chains is analogous to binary computer coding in which each molecule is either a “GC” or an “AT” (rather than a “1” or a “0”). Advanced species have significantly less DNA encoding than primitive species. The amphibian that evolved from a fish no longer needs those parts of its DNA that were exclusive to fish and so loses them. Similarly, the lizard loses those parts of its DNA that were required by amphibians, and so on up the evolutionary scale. Every species carries with it disproportionately huge amounts of inactive DNA that they themselves cannot possibly use. This is for the sole purpose of keeping biological codes in reserve as a backup contingency plan in case of mass extinctions. Every organism is thus preprogrammed with a binary encoded genetic template for what it could evolve to as a species plus endless possibilities for the evolution of new species. Darwinian natural selection is merely a small incidental part of this evolutionary process.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".