Stand competition and slope increase the probability of occurrence of Gremmeniella abietina in forest stands in Spain
Bibliographic record
Abstract
Aim of study: This study aimed to determine the distribution of the fungal pathogen Gremmeniella abietina in forest stands close to the first detection zone in Spain. Additionally, we aimed to identify stand characteristics associated with the presence of the pathogen and evaluate the use of remote sensing methods, such as the Normalized Difference Vegetation Index (NDVI), for early disease detection. Area of study: Province of Palencia, northern Spain. Material and methods: We surveyed 36 forest stands to assess the distribution of G. abietina. Stand inventories, NDVI values derived from Deimos-1 satellite imagery and DNA amplification techniques were used to characterize each stand and determine the presence or absence of the pathogen. Finally, a combination of a random forest algorithm and a permutational multivariate analysis of variance (PERMANOVA) was applied to identify the most significant predictors of the pathogen presence. Main results: The presence of G. abietina was confirmed in 13 plots and was found to be correlated with lower NDVI values during the summer months and with greater competition (i.e., high basal area) on steeper slopes. The algorithm had limited predictive power but it was sufficiently reliable for descriptive purposes. Infected stands also showed higher levels of defoliation, distortion of terminal twigs and dry needles. Research highlights: The results indicate that although the pathogen has spread beyond the initial detection zone, it currently causes only moderate damage. The absence of epidemic outbreaks in the region may be due to environmental conditions that are not conducive to disease development. Nevertheless, the presence of G. abietinacould negatively affect forest productivity, particularly in stands located on steep slopes and characterized by a high level of competition. Use of the NDVI may be a useful tool for detecting trees affected by the pathogen at an early stage, but confirmation through field diagnosis surveys and molecular diagnostics remains essential.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".