Monitoring of Owls and Nightjars, MOON, in Illinois - 2008 Report
Bibliographic record
Abstract
Because anecdotally we know that some species of owls, and possibly all three species of nightjars in Illinois, are declining Monitoring of Owls and Nightjars, MOON, was initiated in 2008.Owls to be monitored during this study were restricted to nocturnal species, because of the time of the study.Therefore, Great Horned Owl, Barred Owl, Eastern Screech-Owl, and Barn Owl were the primary owl species we are monitoring, while Common Nighthawk, Whip-poor-will, and Chuck-will's-widow were the nightjars we are monitoring.Fortunately for us, monitoring programs targeting owls and/or nightjars had already begun in the Northeast (Northeast Coordinated Bird Monitoring Partnership), Wisconsin (Wisconsin Bird Conservation Initiative), Canada (Bird Studies Canada), and the Southeast (U.S. Nightjar Survey Network).This helped us to lay a groundwork protocol so that we would be able to collaborate in the future with these other organizations to try and denote population trends, habitat requirements, and food requirements, and later make sound management decisions to conserve individual species.Being a first year study we knew our volunteer base would not be too robust to begin with, but we hoped it would pique interests as word got out about MOON.We were able to recruit 27 volunteers to run 23 routes.Volunteers created their own 9 mile long routes with 10 stops along suitable owl and nightjar habitat.Because Illinois is so agriculturally dominated using BBS routes was out of the question, as many of them did not fall within habitat that would be used by owls or nightjars.We have historical evidence, because of programs such as Spring Bird Count, Christmas Bird Count, and Breeding Bird Survey, that indicates where owls or nightjars have been detected in the past.There were three monitoring time frames in 2008, one in May, one in June, and one in July.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".