Nearest neighbor training of side effect machines for sequence classification
Bibliographic record
Abstract
Side effect machines operate by associating side effects with the states of a finite state machine. The use of side effect machines permits the researcher to leverage information stored in the state transition structure, making machines that might be identical as recognizers behave differently as classifiers. The side effect machines in this study associate a counter with each state so that the number of times each state is visited becomes a numerical feature associated with each state. The key to effective use of these numerical feature is to locate side effect machines for which the count vectors are good feature sets. In this study side effect machines are selected with an evolutionary algorithm. The Rand index of nearest neighbor classification of the count vectors serves as the fitness function for selecting side effect machines. A parameter study is performed on simple synthetic data and then side effect machines are trained to classify two sets of biological sequences. The first set comprises two categories of HLA sequences from the human major histocompatibility complex. The second are positive and negative examples of human endogenous retroviral sequences taken from the human genome. The retroviral sequences are challenging but good results are obtained. The HLA data is classified with complete accuracy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".