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
Biological networks analysis has become a systematic and large-scale phenomenon. Most biological systems are often difficult to interpret due to the complexity of relationships and structural features. Moreover, existing primarily web-based interfaces for biological networks analysis often have limitations in usability as well as in supporting high-level reasoning and collaboration. Interactive surfaces coupled with tangible interactions offer opportunities to improve the comparison and analysis of large biological networks, which can aid researchers in making hypotheses and forming insights. We present Tangible BioNets, an active tangible and multi-surface system that allows users with diverse expertise to explore and understand the structural and functional aspects of biological organisms individually or collaboratively. The system was designed through an iterative co-design process and facilitates the exploration of biological network topology, catalyzing the generation of new insights. We describe a first informal evaluation with expert users and discuss considerations for designing tangible and multi-surface systems for large biological datasets.
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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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