Evaluating Interior Spruce Seed Deployment with GIS-Based Modeling Using British Columbia’s Prince George Seed Planning Zone as a Model
Bibliographic record
Abstract
Abstract The maintenance, protection, and conservation of forest genetic resources for economic, ecological and social benefits are daunting tasks. Understanding how reforestation materials are spatially and temporally deployed across the landscape is an integral component of forest genetic resources management. To improve the current understanding of how reforestation materials are deployed in British Columbia (BC), we developed a geographical information systems (GIS) method to track seed deployment across silviculture openings. Generally, reforestation materials can originate from either natural stand (wild seed collections) or orchards’ seed sources (improved seed); the latter are produced within the framework of specific tree improvement program designed for a particular species within a well-defined seed deployment area, commonly known as Seed Planning Zone (SPZ). In this paper, we present a GIS-based method for evaluating seed deployment patterns for interior spruce (Picea glauca and Picea engelmannii and their natural hybrids) within the Prince George SPZ. The evaluation period (1970-2004) is associated with wild stands and improved seed availability and the dynamic of each seed source proportionate contribution followed three distinct phases; namely, developing (1970-1987), immature (1988-1994), and mature (1995-2004) with a progressive increase of orchards’ seed use over time. The developed method is scalable across SPZs of the same species or multiple species, thus providing the means to: 1) temporally and spatially monitor improved and natural stands seed deployment over the landscape; and 2) identify areas of concerns where a particular seed source is over-represented which might pose an increased genetic vulnerability. The present study revealed that the current interior spruce orchard’s seed use within the Prince George SPZ is expected to exceed the provincial goal of performance target of 75% by 2014. Additionally, areas of excessive use of one seed orchard seed were identified.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".