Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We investigate the stock composition and seasonal distribution Asian and North American sockeye and chum salmon collected in the Bering Sea during 2002-2004 using genetic markers. For chum salmon, using allozymes and mtDNA, Japanese investigators identified that Asian and North American stocks were not randomly distributed. Japanese stocks were distributed in the central Bering Sea, the distribution of Russian stocks was similar but also spread into the North Pacific Ocean, and northwestern Alaska stocks including fall chum salmon from the Yukon River were distributed mainly in eastern North Pacific Ocean. Using supplemental information, Japanese investigators hypothesize a complete migration model for their hatchery stocks, through the Sea of Okhotsk, seasonally through the Bering Sea and northwestern Gulf of Alaska, back to Japan. For sockeye salmon, using single nucleotide polymorphisms, US investigators identified a broader distribution of North American stocks than suggested by historical tagging studies. Bristol Bay stocks were the most widely-distributed, accounting for more than half the mixtures in all areas except the southwestern Bering Sea. Russian stocks were primarily detected in the western Bering Sea, and differences were detected in the distributions between the eastern- and the western-Kamchatka Peninsula populations. Stocks from the Gulf of Alaska were also widely distributed throughout much of the Bering Sea, although at low proportions relative to the Pacific Ocean-wide production estimates. Data from this project provide the foundation for continuing studies by NPAFC scientists, Pacific Salmon Commission studies by NOAA and ADFG, and are being used by ADFG to improve harvest management in Southeast Alaska, Cook Inlet, and Bristol Bay. These datasets were archived as part of the North Pacific Research Board legacy project recovery effort undertaken by Axiom Data Science and NPRB in 2025. The goal of the recovery effort was to assess the NPRB-funded data projects from 2002 to 2014 and archive final data packages that were ready for publication to increase long-term accessibility and discoverability. Data packages were archived as is given limited funding and resources.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.066 | 0.040 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it