Locally Sourced Seed is a Commonly Used but Widely Defined Practice for Grassland Restoration
Bibliographic record
Abstract
Abstract With continued losses of grassland, the need for grassland restoration increases, and other contemporary threats, such as climate change, may require new techniques for restorations to be successful and resilient. The conservation community has promoted the use of locally sourced seed for grassland restorations, but it is unclear how widespread the practice has become. Furthermore, rethinking how seed is sourced for grassland restorations is one potential strategy to facilitate climate change adaptation. We surveyed practitioners (anyone conducting grassland restorations) across the United States and Canada in 2017 regarding organizational, state/local government, or individual policies for using locally sourced seed in grassland restorations, how local was defined, and whether climate change was considered in these policies and decisions. We received 494 responses from 40 U.S. states and 5 Canadian provinces. Policies and individual decisions supporting locally sourced seed were common, with only 3.6% of practitioners reporting no consideration of local seed sources in restorations. However, the definition of local varied widely, with relatively large geographic areas, such as ecoregions, considered as a local source. Some practitioners considered climate change, but it was not the greatest concern when making seed sourcing decisions. When they did consider climate change, practitioners' most reported strategy was expanding seed zones used for their seed mix. Although there was a heavy upper Midwest bias in the survey responses, the number and geographic scope of responses provides a snapshot of seed sourcing strategies used by practitioners. Our results suggest that practitioners are concerned about maintaining adaptation given the focus on local seed sources, and outreach could be useful to help practitioners incorporate climate adaptation strategies into seed sourcing practices.
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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 | 0.001 | 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 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".