Unsupervised non-linear neural networks capture aspects of floral choice behaviour.
Bibliographic record
Abstract
Abstract. Two unsupervised neural networks were tested to under-stand the extent to which they capture elements of bumblebees ’ unlearned preferences towards flower-like visual properties. The networks, which are based on Independent Component Analysis and Feature-Extracting Bidi-rectional Associative Memory use images of test-patterns that are identical to ones used in behavioural studies. While both models show consistency with behavioural results, the ICA model matches behavioural results sub-stantially better in terms of image reconstruction quality of radial and concentric patterns, and foliage background. Both models generated a novel prediction of an interaction between spatial frequency and symme-try. These results are interpreted to support the hypothesis that flower displays are adapted to pollinators ’ information processing constraints. 1 Information Processing in Bumblebees Bees use visual information to discover their first rewarding flower, but it’s not clear how the visual system aids in this discovery. Hymenoptera species including
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".