Abundance, Distribution and Migration Patterns of North American Eared Grebes (Podiceps nigricollis)
Why this work is in the frame
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Bibliographic record
Abstract
Most North American Eared Grebes (Podiceps nigricollis) undertake a post-breeding migration to two hypersaline lakes in the USA: Mono Lake in California and Great Salt Lake in Utah. Single air photo surveys were conducted in mid-October at Mono Lake from 1996-2012 and multiple fall surveys were conducted from 2013-2018, the latter to determine variation in abundance patterns within and across years. In four of the six years with multiple fall surveys, peak abundance occurred in mid-October as expected. However, in 2014 and 2015, Eared Grebe numbers declined dramatically soon after arrival, coinciding with low levels of their primary food, brine shrimp (Artemia monica). Abundance remained low from 2016-2018, and this could have been due to a shift to Great Salt Lake or to a massive mortality event. In 2017 and 2018, Eared Grebes breeding in south-central British Columbia, Canada were marked with Very High Frequency (VHF) radio transmitters and light-level geolocator (GLS) tags. Contrary to 1996, when the majority of VHF-tagged birds were molting/staging on Mono Lake, our 2017-2018 telemetry data indicated that most individuals were on Great Salt Lake. Our study provides insight into the variable abundance patterns at Mono Lake and novel information on Eared Grebe migration patterns.
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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) | 0.000 | 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 it