Bibliographic record
Abstract
The field usually referred to as mathematical biology is a highly interdisciplinary area that lies at the intersection of mathematics and biology. Classical illustrations include the development of stochastic processes and statistical methods to solve problems in genetics and epidemiology. As the name used to describe work in this field indicates, with “biology” the noun, and “mathematical” the modifying adjective, the relationship between mathematics and biology has so far been one–way. Typically, mathematical results have emerged from or have been used to solve biological problems (see [24] for a comprehensive survey). In contrast, Leonard Adleman, [1], succeeded in solving an instance of the directed Hamiltonian path problem solely by manipulating DNA strings. This marks the first instance of the connection being reversed: a mathematical problem is the end toward which the tools of biology are used. To be semantically correct, instead of categorizing the research in DNA computing as belonging to mathematical biology, we should be employing the mirror–image term biological mathematics for the field born in November 1994. Despite the complexity of the technology involved, the idea behind biological mathematics is the simple observation that the following two processes, one biological and one mathematical, are analogous: (a) the very complex structure of a living being is the result of applying simple operations (copying, splicing, etc.) to initial information encoded in a DNA sequence, (b) the result f(w) of applying a computable function to an argument w can be obtained by applying a combination of basic simple functions to w (see Section 4 or [42] for details). If noticing this analogy were the only ingredient necessary to cook a computing DNA soup, we would have been playing computer games on our DNA laptops a long time ago! It took in fact the ripening of several factors and a
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.023 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".