Regional salmon weight derived from commercial catch data, Alaska, 1975-2016
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
This dataset utilizes commercial catch data (Alaska Department of Fish and Game, Commercial Fisheries Entry Commission. Commercial salmon harvest data: gross earnings and number of fish, Alaska, 1975-2016. Knowledge Network for Biocomplexity. doi:10.5063/F1T151XN) to estimate salmon weight by region, species, and year in Alaska. Regions correspond to the 13 regions used in the State of Alaska's Salmon and People (SASAP) project. These regions do not match up exactly with Alaska Department of Fish and Game regulatory regions, thus both an area and district level dataset from the original dataset were utilized in order to separate some areas into SASAP regions. Missing values in the derived dataset indicate either that there was not a commercial fishery in that region for that species/year, or that the commercial fishery data is confidential. Confidential data is especially problematic in the northern regions (Yukon, Norton Sound, Kotzebue), since there is only one processor that operates in this area during recent years, and according to ADF&G confidentiality rules, data from a year where only one processor operated in an area must be kept confidential. Included in this dataset is the derived weights file, an R Markdown document which calculates the weight data, and two lookup tables used in the aggregation process.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.197 |
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