Bibliographic record
Abstract
I found my experience tour guiding at the Institute for Ocean technology challenging, yet enjoyable as well as rewarding. You always had to be prepared to answer any questions thrown at you, which proved slightly challenging if work was being done on a project you were unaware of the details. Asking your own question to find out all you could was essential in order to relay that information back to tourists. I found that acquiring new information was very interesting and made my job constantly refreshing. The enjoyment of learning new things, meeting new and interesting people and working with friendly coworkers was the highlight of my experience. Being rewarded by gaining valuable work experience was just a byproduct of a very enjoyable employment. My job entailed giving tours for the most part, but also included producing advertising techniques and spreading the advertisements across any form of media available. This included posters, brochures, radio stations, etc. Advertising is a really important part of the job as it garners a huge boost in the amount of tours. The more people know about it the more they are likely to come in for a tour. An underlying portion of the job also includes learning as much information as you can about the Institute and the work going on, so that you can provide that information on the tours. Keeping up on current projects as well as general fields of research that are worked on are a necessity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.201 | 0.085 |
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".