Bibliographic record
Abstract
When a college student comes home for summer, every adult and competitive parent asks two questions: "What are you doing this summer?"and "What are you doing with the rest of your life?"The first question must be answered with the maximum possible pretention and snobbery in order to bring honor to your family.The second question must be answered with excessive humility and self-deprecation but enough of a plan to assure your interrogator that you will not be living in your parents' basement post-graduation.Nothing brings interrogating parents greater joy than hearing that their child is launching more successfully than you; however, if you prove that you are a wholesome, humble, on-the-straight-and-narrow, career bound progeny, they will no doubt smile coldly and reply, "Well, best of luck to you."The summer between my sophomore and junior year of college, my response to the first question went something like this: "I am researching the effects of nitric oxide inhibitor, l-nitro-ωarginine, on plant tissue optics."Atwhich point my mother, with little to no regard for my reputation or self-esteem, would generally interject, "Oh, don't let her fool you.She's counting cucumber hairs and contemplating her navel."With the embarrassment of a kindergartener whose mother packed her overnight underwear "just in case" on her first sleepover, I would meekly retort, "Well, not exactly…" Cucumber seedlings do, in fact, have peach-fuzz like epidermal hairs that, when irradiated with UV light, seem to magically disappear.My job was to count these cucumber hairs using an impressive-looking microscope.Though my evidence was completely inconclusive-there really is no difference
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".