The Potential for Using Spruce (Picea) in Icelandic Forestry
Bibliographic record
Abstract
The aim of this study was to analyze the Icelandic Forest Service’s provenance study of spruces (Picea spp.) that was started in 1995 and 1996 to determine which species and their original sources would be best for afforestation. Experimental plots were divided into two categories: protected (i.e., non-frost-prone) and frost-prone. Survival and height data were collected from nine field sites across Iceland with the last data collection in 2018. Each site had 8 or 10 blocks and every block usually contained 10 seedlings from 14-50 provenances of mostly spruces. The provenances mainly originated from southern Alaska and western Canada. Results indicate that at protected locations, Sitka spruce (P. sitchensis) and Sitka spruce hybrids survive and grow well, while white spruce (P. glauca) and its hybrids have the advantage in frost-prone areas. Provenance 3, a Sitka spruce mixed with Lutz spruce (P. x lutzii) from Iniskin Bay, Alaska, is the only provenance recommended for both types of sites. This information will help afforestation in Iceland in a way that brings environmental advantages and sustainable practices, such as minimizing soil erosion, increasing carbon sequestration, and reducing the risks of invasive species. Furthermore, it can contribute to the development of a viable timber industry that benefits local and national economies through sustainable resource management. Finally, forestry strategies need to consider climate change to ensure long-term success of afforestation efforts in Iceland. \n \nKeywords: spruce, provenance, frost-prone, afforestation, Iceland
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".