Bibliographic record
Abstract
JOHNSON CITY (May 26, 2020) – Connect Outdoors, an affiliate member of East Tennessee State University’s Innovation Lab, will host the Big Bass Shootout through its Connect Fishing League. A series of four one-day tournaments open to all ages is scheduled for May 30, June 6, June 13 and June 20 with the winner to be announced on Father's Day, June 21. The Big Bass Shootout is a single best catch Black Bass tournament (Largemouth Bass, Smallmouth Bass or Spotted Bass). Fishing can take place in any public waterway in the U.S. or Canada. Fishing in private ponds is not allowed. All catches must be weighed with a ConnectScale, a Bluetooth smart digital fishing scale created by Connect Outdoors that easily interfaces with a smartphone. No other weighing devices are allowed. This is an open tournament for anglers of all levels and ages. Participants can enter one or more rounds. Multiple entries increase chances to win. A $50 bonus prize pack will be awarded if the winner is a Connect Fishing League member. Instructions for joining the tournament are available at connectfishingleague.com/event-3849866. Connect Outdoors, based in Johnson City, is dedicated to providing tools that bring people together outdoors. Their products are focused on the sportfishing industry and creating tools for recreational and tournament anglers to make fishing experiences more efficient and enjoyable. The Connect Fishing League recently hosted the Northeast Tennessee Open Fishing Tournament. For more information or to join the league, visit connectfishingleague.com. The ETSU Innovation Lab, located at 2109 W. Market St., is a high-tech business incubator that assists aspiring entrepreneurs in developing their ideas from concept to commercialization. For more information about the ETSU Innovation Lab, visit http://www.etsu.edu/ilab.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.662 | 0.425 |
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".