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
In this study, we investigate and characterize haptic interaction in human-to-human handovers and identify key features that facilitate safe and efficient object transfer. Eighteen participants worked in pairs and transferred weighted objects to each other while we measured their grip forces and load forces. Our data show that during object transfer, both the giver and receiver employ a similar strategy for controlling their grip forces in response to changes in load forces. In addition, an implicit social contract appears to exist in which the giver is responsible for ensuring object safety in the handover and the receiver is responsible for maintaining the efficiency of the handover. Compared with prior studies, our analysis of experimental data show that there are important differences between the strategies used by humans for both picking up/placing objects on table and that used for handing over objects, indicating the need for specific robot handover strategies as well. The results of this study will be used to develop a controller for enabling robots to perform object handovers with humans safely, efficiently, and intuitively.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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