Development and testing of mechanistic fitness-based models to predict habitat choice, behavior, and recruitment of juvenile Chinook salmon in the Arctic-Yukon-Kuskokwim region
Bibliographic record
Abstract
This data is part of the NPRB research project 1424. Specifically, these data describe varying prey capture success rates of juvenile Chinook salmon in response to our main variables of water velocity and dominance hierarchy. The complete dataset consists of six csv files. The data consists of 1. Single fish prey capture data from experiment using that used 2D measurements, 2. Single fish 2D microhabitat measurements from our experimental flume, 3. Single fish prey capture data from experiment using 3D measurements, 4. Dominance experiment data from prey capture trials recorded by an observer, 5. Dominance experiment 3D data extracted from the VidSync software, and 6. The lengths, mass, and days in captivity for our fish used in the dominance experiments. Juvenile fish were captured in the Chena River, Alaska, using minnow traps in 2015 and experiments were conducted beginning in January 2015 and ending in July 2015 when video analysis was completed. Chinook Salmon (Oncorhynchus tshawytscha) are a native salmonid to the region and a very popular fish for anglers. Over the past decade their populations have begun to decline due to a variety of factors including climate change, urbanization, and predation. Data are presented as one CSV file: Chinook_SingleFish_3D_Behavior.csv
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".