One step ahead of the plow: Using cropland conversion risk to guide Sprague's Pipit conservation in the northern Great Plains
Bibliographic record
Abstract
Rapid expansion of cropland threatens grassland ecosystems across western North America and broad-scale planning can be a catalyst motivating individuals and agencies to accelerate conservation. Sprague's Pipit ( Anthus spragueii ) is an imperiled grassland songbird whose population has been declining rapidly in recent decades. Here, we present a strategic framework for conservation of pipits and their habitat in the northern Great Plains. We modeled pipit distribution across its million-km 2 breeding range in Canada and the U.S. We describe factors shaping distribution, delineate population cores and assess vulnerability to future grassland losses. Pipits selected landscapes with a high proportion of continuous grassland within a relatively cool, moist climate. Sixty percent of the global breeding population occurred in Canada and 63% of the U.S. population occurred in Montana. Populations were highly clumped, with 75% of birds within 30% of their range. Approximately 20% of the population occurred on protected lands and over half used lands that were unlikely to be cultivated given current technologies. A quarter of pipits relied on remaining arable grasslands and potential population losses varied from 2–27% across scenarios. Most of the population (70%) was dependent on private lands, emphasizing the importance of voluntary approaches that incentivize good stewardship. Our maps depicting core populations and tillage risk enable partners to target conservation in landscapes where pipits will benefit most.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".