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
An inbred laboratory strain (W-strain) of <i>Lymnaea</i><i>stagnalis</i> is capable of configural learning. Configural learning, a higher form of learning, is an association between two stimuli experienced together that is different from the simple sum of their components. In our configural learning procedure, a food odour (carrot odour, CO) is experienced together with crayfish effluent (CE) (i.e. CO+CE). Following configural learning, CO now elicits a fear state rather than increased feeding. We hypothesized that freshly collected wild strains of predator-experienced <i>L. stagnalis</i> also possess the ability to form configural learning, even though they experience crayfish daily in their environment. We therefore subjected freshly collected wild strain <i>L. stagnalis</i> to the configural learning procedure. Following the configural learning procedure, CO became a risk signal and evoked anti-predator behaviours. Thus, configural learning was demonstrated in wild, freshly collected snails. We believe that configural learning occurs in the snail's natural environment and is important for their survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it