Using spruces (Picea spp.) for Icelandic afforestation
Bibliographic record
Abstract
Afforestation can help address climate change and biodiversity loss. Iceland is a valuable case study to assess afforestation at extreme locations at high latitudes (63–68°N). We used a ~ 23-year dataset of a provenance trial from the Icelandic Forest Service (now Land & Forest Iceland) to determine the best spruce species ( Picea spp.) and provenances for afforestation. Sites were either frost-prone or protected (i.e., non-frost-prone) locations, and the latest height and survival data were assessed from six sites (out of nine) in 2018. Provenances were mainly from three spruce species from southwestern Canada and southern Alaska (53–61°N). Sitka spruce ( Picea sitchensis ) and its hybrids or introgressants with white spruce ( P. glauca ) survived and grew well in protected areas (≥ 60% and ≥ 275 cm), while white spruce and its hybrids or introgressants with Sitka spruce performed better in frost-prone areas (≥ 55% and ≥ 285 cm, based on combined frost-prone sites). The only provenance suitable for both frost-prone and protected places was a Sitka/Lutz spruce ( P. x lutzii ) introgressant from Iniskin Bay, Alaska. Additional genetic research would help guide afforestation in harsh areas at high latitudes (a distributional limit of many tree species) and inform forest management about novel environments and sustainable practices. Climate change should also be considered for afforestation efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
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".