The applicability of national forest inventories for estimating forest damage outbreaks – Experiences from a <i>Gremmeniella</i> outbreak in Sweden
Bibliographic record
Abstract
The design of the Swedish National Forest Inventory as well as the National Forest Damage Inventory is a sparse sample of systematically allocated plots. In this study data were combined from these two independent inventories to estimate geographical distribution, area extension, and disease development of a Gremmeniella abietina (Lagerb.) Morelet epidemic. For the combined data the standard error for estimated total area of affected Scots pine (Pinus sylvestris L.) and lodgepole pine (Pinus contorta Dougl. ex Loud. var.latifolia Engelm.) forests was about 11%. Assessments of the proportion of pine trees with fresh shoot blight infection shows that changes larger than 1% are significantly (p < 0.05) estimated. By testing in pairwise cross inventory, it was shown that the accuracy of the assessment of total shoot blight symptoms was fairly good in 2001–2002, with a κ statistic of 0.59–0.61. Severely damaged trees were identified with an agreement of κ = 0.81. The total area of pine forest both slightly and severely affected by G. abietina during 2001–2003 was estimated to be 484 000 ha. Three geographically separate damage centres were distinguished. Thus, despite a relatively sparse sample plot density, the national forest inventories have good potential for estimating the geographical distribution, areal extent, and dispersal of extensive damage outbreak. Results are dependent on the inventories being carried out with an accurate identification of target objects.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".