Bibliographic record
Abstract
Last year I was co‐investigator on an award winning research project that sought to improve sustainable energy technology. Our project optimized dye‐sensitized solar cells, which are cheaper and more environmentally friendly than traditional solar cells. We discovered that adding a certain material results in approximately a 10% increase in efficiency. Although I would like to, I cannot share any more details because of intellectual property issues. This seems odd considering the intent of the project; however, it is a common and valid concern for researchers because of current intellectual property law. An inventor has no claim to an invention until it is patented. The expense of patenting forces people to hoard their ideas even if they do not want the patents for themselves. Though ideas could benefit society, inventors must conceal them lest a greedy entity patent them and prevent their free use. To remedy this problem I propose the establishment of an institution that would pay for patent applications, provided that after the patent is granted only a minimal fee is charged for use of the patent. This fee would compensate the inventors reasonably, pay upkeep costs of the institution and possibly fund a grant agency. This would encourage innovation by allowing free exchange of ideas without fear of intellectual robbery or loss of credit to the inventor, facilitating more productive and expedient research. The institution would afford society virtually free use of technologies with the consent of the inventor, making widespread implementation of new technologies more feasible.
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 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 | 0.042 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".