Bibliographic record
Abstract
The need for genomes to detect and correct errors has been a major force in evolution, driving DNA to ‘speak in palindromes’ and splitting members of a species into two sexes. Errors associated with DNA mutations or DNA damage may be imperfectly corrected in our bodies (“soma”), but should be better corrected in the germ line. To this end, meiotic recombination repair, in which maternal and paternal genomes are compared, is the last court of appeal. For this ‘rebooting’ of the genome, parental genomes must be neither too similar, nor too disparate. If too similar (inbreeding), differences will not emerge at meiosis in their child’s gonad. If too disparate, an exploratory speciation process (anti-recombination) may initiate, manifest as a healthy, but sterile, child (hybrid sterility). For the initiation of speciation, human females must: (i) differentiate their sex chromosomes (X and X), (ii) differentiate their non-sex chromosomes (22 autosomal pairs), and (iii) activate ‘check-points’ which respond to such differentiations by disrupting meiosis. In contrast human males, being already advanced in the first step due to differentiation of their sex chromosomes (X and Y), have to complete only the latter steps. Thus, the first sign of speciation (incipient speciation), manifest as hybrid sterility, is production of sterile males (Haldane’s rule). Long ago, anti-recombination activity prevented repair of Y-chromosomes, which degenerated, thus loosing many X-chromosome equivalent genes. Human males now have potentially only one dose of many X-chromosome gene products, whereas females have potentially two doses. Dosage compensation in human females, leaving only one X-chromosome active, buffers fluctuations in intracellular protein concentrations between male and female generations. This permits a gene, independently of the sex which may harbor it, to fine-tune the concentration of its protein product to that of other proteins with which it has been traveling through the generations. In this way, collective protein functions, including intracellular self/not-self discrimination, are facilitated. Failure of this may predispose human females to autoimmune disease. By virtue of extensive palindromic sequences, Y chromosomes appear, like some rare ameiotic organisms, capable of in-series error-correction. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.037 | 0.018 |
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".