Nitrogen and Ergosterol Concentrations Varied in Live Jack Pine Phloem Following Inoculations With Fungal Associates of Mountain Pine Beetle
Bibliographic record
Abstract
Bark beetles (Coleoptera: Curculionidae, Scolytinae) contain some of the most ecologically and economically important insect species of conifer forests due to their ability to kill a large number of trees during periodic outbreaks, with cascading consequences for ecosystem function (Kurz et al., 2008; Bentz et al., 2010; Hicke et al., 2012; Seidl et al., 2014). Fungal symbionts associated with bark beetles can amplify the beetles’ damage by promoting successful host colonization and development (Harrington, 2005; DiGuistini et al., 2007; Six and Wingfield, 2011; Six, 2012; Wang et al., 2013, 2014; Therrien et al., 2015; Cale et al., 2017; Zhao et al., 2019). In particular, fungal symbionts can facilitate beetle nutrition by either serving as a direct dietary substrate (e.g., ergosterol) , or indirectly concentrating host plant nutrients (e.g., nitrogen) (Ayres et al., 2000; Bleiker and Six, 2007; Goodsman et al., 2012). Although bark beetle species have multiple species of symbiotic fungi (Klepzig and Six, 2004; Klepzig et al., 2004; Roe et al., 2011a), how these fungi differ in the nutritional benefits they provide to the beetles is poorly understood.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".