Data and analyses for Knight et al. 2021 Ornithological Applications
Bibliographic record
Abstract
Script, data, and models to run analyses for Knight, E.C., A.C. Smith, R.M. Brigham, and E.M. Bayne. 2021. Combination of targeted monitoring and Breeding Bird Survey data improves population trend estimation and species distribution modeling for the Common Nighthawk For trend estimation: the analyses compare trends estimated from simulated data for the Canadian Nightjar Survey (CNS) and the North American Breeding Bird Survey (BBS). The main script file "TrendSimulation.R" inputs the four data files, defines and runs the hierarchical model used to parameterize the simulations, generates the simulated data, simulates the trend estimates, and plots the results. Files for trend estimation are prepended with "Trend_", which will need to be removed prior to running scripts. For species distribution modelling: the analyses split data, build SDMs, predict on withheld data, and evaluate model performance for the CNS and BBS. The main script file "HabitatModel.R" runs through three analyses for each of three bird conservation regions (BCRs). The three analyses are similar, with differences in habitat covariates for each BCR. Each analysis requires 3 data files and 3 large raster stacks (for prediction).
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.600 | 0.370 |
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".