Bibliographic record
Abstract
We are currently living in a world surrounded by technology, making us very dependent on the little nitty gritty way of accomplishing tasks efficiently. Being mindful of the of the changes that are occurring upon us, we are compelled to helping where possible. Personally, Sheridan College pilots their very own campus radio station – Sheridan Life Radio (SLR) a member of the National Campus and Community Radio Association, whose main aim is to bring a little bit of everything to the table covering special occasions such as Canadian upcoming holidays, Valentine’s Day, Mother’s Day to motivational and inspirational podcasts. SLR primarily doesn’t only produce content for the college but for podcasting platforms such as Spotify, Google Podcasts and Apple Podcast. Hence, keeping in mind of the wide variety of topics that the SLR tackles, the most troublesome one happens to be the management of podcasts. Where the mode of operation for them currently is to have individuals chase producing – the act of finding guests, stories and angles and collaborative podcasting process. This process can be nerve-wreaking and frustrating. To help SLR overcome this problem a proposed solution is to have procuring audio clips to one stop location for the clip gathering cutting the stress and effect of people personally being out and about in the field. Since the proposed solution aims to make the way of operation smarted and less stressful for the SLR team it is called Co-Cast.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.440 | 0.349 |
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".