Location serves as a conditional cue when harp seals (Pagophilus groenlandicus) solve object discrimination reversal problems.
Bibliographic record
Abstract
We examined the capacity of harp seals (Pagophilus groenlandicus) to use spatial context (i.e., their tank) as a conditional cue to solve a two-choice visual discrimination reversal task. Seals were trained to touch one of two 3D objects. Two of four seals experienced a context shift that coincided with each of five reversals in the reward value of the two stimuli (i.e., a reversal of S+ and S-); these seals solved the six discriminations in significantly fewer trials than did seals that did not experience a context shift with the contingency reversal. Thus, harp seals use contextual cues when encoding information. The findings are discussed in terms of harp seals' adaptations to the pack-ice environment, the constraints of the learning tasks, and the nature of the subjects that were raised in captivity.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".