A Model For Sea Lice (Lepeophtheirus salmonis) Dynamics In A Seasonally Changing Environment
Bibliographic record
Abstract
Updated March 30th, 2016 This project contains R code to replicate the results of our modelling research on sea lice. The file, Marty_Data.csv, is a .csv conversion of data from Marty et al. (2010), DOI: 10.1073/pnas.1009573108. The file, RittenhouseEtAl_eggviability, fits a linear model to salinity and egg viability data from Johnson & Albright (1991). The file, RittenhouseEtAl_ModelDynamics.R produces figure 2 and figure 3 of our manuscript. The file, RittenhouseEtAl_SensitivityAnalysis.R, produces our sensitivity analysis results and figure 5 of our manuscript. The file, RittenhouseEtAl_SiteEnvironment, fits sinusoidal models to temperature and salinity data from British Columbia (Marty_Data.csv) and Newfoundland (not included), and finds the the 5th, 50th, and 95th percentiles of the temperature and salinity data at these two sites. The file, RittenhouseEtAl_R0t, finds Re(t) for the two sites as well as mortality rates and maturation times. The file, RittenhouseEtAl_CaseStudyFigure creates figure 4 of the main text. For legal reasons, we unfortunately can not include data from our Newfoundland case study site. As such, errors will be returned on the RittenhouseEtAl_SiteEnvironment.R and RittenhouseEtAl_CaseStudyFigure.R files. The information for the British Columbia site, as well as panels A, B, and C of figure 5 are still able to be replicated without this data. The files, RittenhouseEtAl_SiteEnvironment.R and RittenhouseEtAl.R0t.R should be run before RittenhouseEtAl_CaseStudyFigure.R. The files are meant to be placed in the same file and run in the following order and manner: rm(list = ls()) # clear workspace setwd() # Set this to folder's location source("RittenhouseEtAl_eggviability.R") rm(list = ls()) # clear workspace setwd() # Set this to folder's location source("RittenhouseEtAl_ModelDynamics.R") rm(list = ls()) # clear workspace setwd() # Set this to folder's location source("RittenhouseEtAl_SensitivityAnalysis.R") rm(list = ls()) # clear workspace setwd() # Set this to folder's location source("RittenhouseEtAl_SiteEnvironment.R") rm(list = ls()) # clear workspace setwd() # Set this to folder's location source("RittenhouseEtAl_R0t.R") rm(list = ls()) # clear workspace setwd() # Set this to folder's location source("RittenhouseEtAl_CaseStudyFigure.R")
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".