Evaluating interior spruce genetic resource management practices through GIS-based tracking of seed deployment over time in British Columbia
Bibliographic record
Abstract
To improve current understanding of how genetically improved stocks are spatially and temporally deployed, tree breeders, gene resource managers, and forestry research community can gain insights from an analysis of currently available geospatial data. This analysis can also help and provide the raw material for better understanding of the relationship between gene resource management (GRM) and issues related to climate change and other risks such as the current mountain pine beetle epidemic. GIS links the latest information management concepts and methods to assist GRM achieve higher genetic gain, resilience and conservation goals. According to the Chief Forester's standards for seed use and the target of the Forest Genetics Council of BC, 75% of seed use will be from selected seed sources. This research developed a GIS based method to monitor and assess the spatial temporal variability of seed deployment in one seed planning zone of interior spruce and employed map representations to visualize the spatial cluster dynamics of reforestation plantations in BC. The investigation of deployment areas and stem number of seed stock inform our knowledge of forest recovery in the context of gene resources management. Class A and B⁺ seed use increased dramatically after 1995 in the Prince George Seed Production Zone (SPZ). The A class ratio in PG total seed deployment is 48.1% from 1995 to 2004 with seed orchard 214 being the leading seed source for Sx reforestation in PG at the SPU level. The Sub-boreal spruce zone is the main natural habitat area of Sx, where intensive forest management activity undergoes as the plantation hotspots. Observed changes in genetic class indicate the intensive reforestation with selected seed sources. The applicability of GIS modeling methods is available at different SPZ levels and species scopes. The system construction of an updated GRM criteria and decision making support is noteworthy for BC foresters to more wisely harvest, recover, and manage the forests in a more sustainable manner.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".