R code and data from: Preserving identity in capture-mark-recapture studies: Increasing the accuracy of minimum number alive (MNA) estimates by incorporating inter-census trapping efficiency variation
Bibliographic record
Abstract
Each file is summarized below: Abundance estimation script.R: an R code file with detailed workflow for computing and testing a number of abundance enumeration metrics according to the text laid out in "Preserving identity in capture-mark-recapture studies: Increasing the accuracy of minimum number alive (MNA) estimates by incorporating inter-census trapping efficiency variation". Prepared badger data.RData: R objects prepared from badger data, used in the analysis from the paper. Comprises the following objects: - trueAlive, a matrix with rows for individuals and columns for years. 1s indicate an individual was alive in a given year; 0s indicate it was not. - trueAge, a matrix with the same structure as trueAlive, but where each position indicates the individual's age in that year. - trueTeeth, a matrix with the same structure as trueAlive and trueAge, but where each position indicates the toothwear of the individual on a scale of 1 to 5. - toothAgeMod, a model that contains the relationship between tooth-wear and age. - trueSex, a vector with the sex of each individual corresponding to the row in the life-history and context matrices.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.133 | 0.138 |
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".