Bibliographic record
Abstract
Census Data Quality Research By Ian McKechnie For the Global Burden of Animal Diseases Check us out! GBADsKE Description of this roject As the saying goes, "Garbage in, Garbage out". This project is to evaluate the quality of data from the FAOSTAT, WOAH, UN Census, and individual country data for future modelers to understand the quality of the data before they use it in their models. You can read the report on the findings from using the tools in this repo. Findings from this tool are currently pending review before being published. Stay tunned on the GBADsKE website for more information. Project Requirements Python V3.10 To use this project Run these commands in the project folder you must be using python3 with a version <3.11. (I used python3 version 3.10 for development) cd src pip3 install -r requirements.txt python3 app.py Data sources FAOSTAT and WOAH/OIE come from the API Census data from countries is in the S3 bucket (on AWS) National data needs to be harvested from Stats agencies of countries Counties Ethiopia Canada USA Ireland India Brazil Botswana Egypt South Africa Indonesia China Australia New Zealand Japan Mexico Argentina Chile Possible species that can be viewed (depends on country) Cattle, Beef Cattle, Dairy Cows, Sheep, Goats, Pigs, Chickens, Horses, Buffaloes, Ducks, Turkeys, Ostrichs, Asses and Mules, Mules, Asses, Wild Boars, Boar, Bison, Elks, Llamas/Alpacas, Alpacas and Llamas, Ostriches and Emus, Alpacas, Llamas, Deer, Minks, Foxes, Rabbits, Other Fowls, Geese, Guinea Pigs, Poultry, Camels, Pigeons, Geese and Ducks, Bees, Beehives, Mithuns (Bovine), Equines, Broilers, Laying hens, Hen, “Mules, Asses”,
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 | 0.032 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.138 | 0.090 |
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".