Bibliographic record
Abstract
The eagles watch project is an effort of volunteer bird observers collecting data to monitor the Golden Eagle population in the Rocky Mountains of Calgary, Canada. The project began in March 1992, through April 2012. Such a Citizen Scientist research project has gained great popularity over the last decade due to extensive labor and time needed for observational studies in the fields such as Ornithology and Astronomy. The goal of the Citizen Scientist project is not only accelerate and enrich the scientific discovery, but also to promote public awareness in scientific matters. However, a critical challenge facing on these type of projects which is how to ensure data quality from Citizen Scientists. Particularly in this project, the timespan and frequency of observations made by volunteers vary due to uncontrollable factors, and there may also be the varying degrees of proficiency in identifying the Golden Eagles in migration among different observers. This study investigates these effects on data collection using mixed effect modeling and kernel smoothing. Among 38 volunteers, 23 observers were found to have mean residuals statistically significant different from zero, suggesting potential problem with these observers. Data from the remaining observers revealed a general trend of decreasing eagle population over the past 20 years, though this trend is not statistically significant.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".