Continuing Declines of Grassland Birds in California’s Central Valley
Bibliographic record
Abstract
Grassland birds of North America are in more rapid and widespread decline than those of any other habitat guild. While most population trend data are based on breeding season surveys, this decline is also evident from studies in the non-breeding season. A study using Christmas Bird Count (CBC) data from California’s Central Valley showed highly significant declines in nearly all grassland-associated birds from the late 1970s to 2014 (Pandolfino and Handel 2018). We used data from the same Central Valley CBCs used in that study to demonstrate that this decline has continued through at least 2019 for the American Kestrel (Falco sparverius), Loggerhead Shrike (Lanius ludovicianus), Horned Lark (Eremophila alpestris), American Pipit (Anthus rubescens), Lark Sparrow (Chondestes grammacus), and Western Meadowlark (Sturnella neglecta). The rate of decline for the Loggerhead Shrike and the Horned Lark may have increased in recent years. We also compared the rate of these declines to the loss of grassland habitat in the CBC circles and discuss some of the implications of these findings. The one wintering grassland species that showed a positive trend in earlier studies, the Say’s Phoebe (Sayornis saya), continued to increase in abundance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".