Damage Agents and Condition of Mature Aspen Stands in Montana and Northern Idaho
Bibliographic record
Abstract
Michx.) forest acreage unit in Ogdensince European settlement (Bartos 2001). Data from the U. S. Department of Agriculture, Forest Service (USFS) Forest Inventory and Analysis (FIA) unit in Ogden, Utah suggest aspen acreages within Montana and Idaho are down 64% and 61% since settlement, respectively (Bartos 2001). dAspen stand health has also shown declines role of varioussince the 1970’s. Two primary forces are most commonly cited as contributing to this decline; changes in fire regimes since European settlement and heavy ungulate browsing leading to inadequate regeneration (for example see Romme et al. 1995, Kay 1997, Bartos and Campbell 1998). More recently, severe and rapid dieback and mortality of aspen in Colorado, as well as Alberta, Saskatchewan, and Manitoba, Forest Health Canada have been tied to drought (Hogg et al. 2008, Worrall et al. 2008). Forest diseases and insects are often notable as potential contributing or inciting factors (Frey et 4) but play a largely undefined role in the -term permanent monitoring plots established by the USFS FIAconfirmed the severity and extent of suspected decline symptoms and deterioration of aspen forests throughout its range in the Rocky Mountains from Canada to Mexico (Shaw 2004). The publication also recommended establishment of additional off-plot sites to further define extent and severity of ecline in aspen clone health and examine the damage agents.Funding provided by USFS Evaluation Monitoring (project INT-F-06-01) allowed establishment of permanent monitoring plots in aspen stands in Nevada, Utah, southern Idaho and western Wyoming (USFS Region 4) in 2006 and 2007, and west and central Montana, andnorthern Idaho (USFS Region1) in 2008. Surveys were to supplement established FIA Monitoring plot system efforts by providing additional data on forest damage/decline agents in aspen forests. Only results from Region 1 are reported here.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".