Bibliographic record
Abstract
Over the past quarter century, my engagement with the protein society has allowed me to witness first-hand the evolution of our deepening understanding of the complexity of protein folding landscapes. During my own evolution as a protein scientist, my passion for protein folding has deepened into an obsession with mapping and decoding the thermodynamic and kinetic secrets of protein landscapes-especially those of rebel proteins, whose "nontraditional" behavior has challenged our paradigms and inspired the expansion of our models and methods. It is perhaps not surprising that I see parallels in the evolution of the landscape framework and in the development of our own trajectories as humans in Science, Technology, Engineering and Mathematics (STEM). Just as with proteins, however, we need to recognize that our individual human landscapes are not isolated from our local departmental and institutional communities, and are integrated into the larger networks of our STEM disciplines, academia, industry, and/or government, not to mention society. My experience with hundreds of participants in the Being Human in STEM (HSTEM) initiative that Amherst College undergraduates and I co-founded in 2016 has helped me find hope for STEM and humanity. If we commit to reconciling our identities as scientists with our responsibilities as human beings, together we can accelerate the evolution of individual, community, and societal landscapes to contribute to addressing the dire challenges facing our planet.
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.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".