Analysing knot evidence: associating innate habits with sophisticated tying tasks
Bibliographic record
Abstract
Abstract Most ligature cases feature everyday, innately-tied Overhand Knots, Half Hitches and Half Knots. These knots are the result of habitual behaviour and individual tiers demonstrate consistency, except when certain contextual factors come into play. This survey focussed on comparing the chiralities of basic knots to those of Figure Eight Knots, which occur in case evidence and require similar tying actions. It is important to note that real-world Figure Eights are oriented relative to their working ends and are therefore chiral, whereas topological Figure Eights have no ends and are amphichiral. Data summarizing the tying habits of 184 survey respondents were collected and analysed. The majority of volunteers surveyed tied common Overhand Knots and Figure Eights of equal chirality, consistently or nearly consistently, irrespective of any general learning effect. A minority tied knots of opposite chirality. The knots tied by the remaining respondents varied, and the data suggested a potentially complex pattern which may be related to previous findings. Similar but less pronounced patterns were exhibited in the Half Hitch and Half Knot data. This information could be useful when analysing case evidence and making links to suspect samples, provided cautious attention is paid to context and knot function.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".