Landscape-level likelihood estimation of eastern spruce dwarf mistletoe (<i>Arceuthobium pusillum</i>) infestations in lowland black spruce (<i>Picea mariana</i>) forests of Minnesota, USA
Bibliographic record
Abstract
Biotic disturbance agents are important factors influencing forest dynamics; incorporating them into management planning requires a detailed understanding of their distribution, prevalence, and effects on stand dynamics. However, this information can be difficult to collect in remote forest systems, such as lowland black spruce ( Picea mariana (Mill.) B.S.P.) forests affected by eastern spruce dwarf mistletoe ( Arceuthobium pusillum Peck, hereafter ESDM). In such cases, predictive modeling is often needed to inform management decisions and address forest health questions. Here, we used two publicly available datasets to predict areas where black spruce is more likely to be infested with ESDM in northeastern Minnesota, USA. Using random forest modeling and logistic regression, we found location, stand age, basal area, site index, average diameter, and metrics of species composition to be among the most important predictors of ESDM occurrence. Predictions showed two regions of greater likelihood of infestation with distinct ecological characteristics and ownership patterns. By understanding how stand structural characteristics relate to ESDM infestations, managers can improve monitoring and management of ESDM at the stand and landscape scales. Additionally, our approach of using multiple datasets and modeling methods can serve as a framework for decision-making on other forest health concerns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".