GENOMIC ORGANIZATION AND HOPFIELD'S MODEL OF ASSOCIATIVE MEMORY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We consider a description of genomic organization based on a neural network model of associative memory (Hopfield's model). This description is consonant with the view that the cell cycle involves transitions between successive phases, each entailing an assortment of metabolically distinct states. A picture emerges where a cell at a particular state in its life cycle fulfills its metabolic needs through a specific set of genetic patterns, comprising a characteristic, stable one, plus those belonging to its basin of attraction in the sense of the neural network metaphor. A gene may belong to more than one stable configuration, so its function will depend on its context as defined by the particular pattern playing a dominant role at any given moment. The model provides a conceptual framework in genomic analysis and yields quantitative results in some cases, that can be assessed using observable data. For example, it allows consideration of a genome's resilience under variation of its genetic interactions. It also suggests an upper bound on the number of stable genetic patterns in prokaryotes and archaeons, close to 14% of their total endowment of genes. We test this estimate against available information from sequenced genomes.
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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) | 0.000 | 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 it