Supplement 1. Details of the analysis of pink salmon population data, including spawner and catch data and R code for compiling spawner-recruit pairs and fitting the Ricker model.
Bibliographic record
Abstract
File List nuSEDS_PINK.csv (md5: 817593c4467c074239177e92379db2bd) Catch_PINK.csv (md5: a9442b3ebafbf77d5437e36b6b0faad3) 1_DataCompilation.R (md5: 1eb5bac1081e5ac2f307b00e4a75c8ef) 2_PopulationAnalysis.R (md5: 017c4ea62556877b4b028bb2b4dc947d) Description The following files are made available with the intention of ensuring our manipulation of data and analysis of pink salmon populations is entirely transparent. We assume that the reader is familiar with the program R, which can be downloaded from the web at cran.r-project.org. Any errors in transcribing and manipulating the data are the sole responsibility of the authors. Additional data and code for the analysis of farm treatments and lice on wild salmon are available upon email request to Stephanie Peacock [stephanie.peacock@ualberta.ca]. nuSEDS_PINK.csv contains spawner data for pink salmon populations in British Columbia, Canada from 1950–2010, provided by Fisheries and Oceans Canada (contact: Bruce Baxter [bruce.baxter@dfo-mpo.gc.ca]). These data include the following columns: Area: Fisheries management area, the spatial scale at which catch data are reported. River: The river at which spawners were enumerated, the finest scale at which spawner data are available. Yr: The year spawners were enumerated. Escapement: The estimated number of spawners in that river in that year, from the nuSEDS database. Catch_PINK.csv contains catch data for pink salmon by management area, provided by different area managers at Fisheries and Oceans Canada (contacts: Pieter VanWill [pieter.vanwill@dfo-mpo.gc.ca] and David Peacock [david.peacock@dfo-mpo.gc.ca]). These data include the following columns: Area: Fisheries management area, the spatial scale at which catch data are reported. Odd_Even: Integer indicated whether the catch was of an odd-year population (= 1) or even year (= 2). Year: The year of the catch. Catch: The number of pieces of pink salmon caught in all fisheries for the given year and area. 1_DataCompilation.R is R code that calls the previous two data files and calculates the number of recruits per spawner. 2_PopulationAnalysis.R is R code that fits the linearized Ricker model to the spawner recruit data, and tests for an effect of sea lice on wild salmon.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.746 | 0.304 |
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".