Should I pick that? A scoring tool to prioritize and valuate native wild seed for restoration
Bibliographic record
Abstract
Commercial sources of native seed are often unavailable for ecological restoration projects or do not have a suitable provenance. Local collection of wild seed is an option, but it can be challenging to collect seed for a variety of species and set fair seed prices. Our aim was to quantify the relative effort to collect, clean, store, and propagate seed to better prioritize species and assess the value of their seed. For 57 species native to the Canadian subarctic and typical of upland habitats, we evaluated 13 poorly correlated attributes in the field and lab or using the literature. For collection attributes, regional occurrence, local abundance, seed collection rate, and collection window were normally or log‐normally distributed. Most species were easy to identify and posed few collection obstacles. Cleaning effort was evenly distributed across species and the majority could be cleaned to more than 95% purity. We only encountered orthodox seed and most species had seed longevity exceeding a year. Seed viability mostly exceeded 80%, pre‐treatment requirements were evenly distributed and the majority of species could be germinated under standard conditions. We propose a standard worksheet, in which we assign relative effort scores to the distribution of each attribute. We illustrate this approach for the revegetation planning of a remote mine site. We also propose a seed lot certificate to ensure high seed quality. This tool can be applied to various restoration applications to assess relative effort, to plan and prioritize species for restoration projects and to help set fair seed pricing.
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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) | 0.000 | 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".