Continuous Detection PIT Array Data & Model
Bibliographic record
Abstract
These data represent a set of capture histories of rainbow trout (Oncorhynchus mykiss or RBT) captured in the Colorado River (CR) and(or) detected on the multiplexer array in the Little Colorado River (LCR). Capture trips to the Colorado River occurred in April 2012, July 2012, September 2012, January 2013, April 2013, July 2013, September 2013, January 2014, April 2014, July 2014, and September 2014. Rainbow trout were detected on the PIT array system (MUX) from October 2013 - April 2014. These data are associated with the journal manuscript: Dzul, M.C., Yackulic, C.B., and Korman, J., 2017, Estimating disperser abundance using open population models that incorporate data from continuous detection PIT arrays: Canadian Journal of Fisheries and Aquatic Sciences, early view available at https://doi.org/10.1139/cjfas-2017-0304.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.068 |
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; both teacher heads agree on what is shown here.
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".