Fungal trunk diseases: a problem beyond grapevines?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Grapevine trunk diseases (GTDs) are caused by a broad range of taxonomically unrelated fungi that occur wherever grapes are grown and are the main biotic factor limiting vineyard productivity and longevity (Bertsch et al., 2013). GTDs cause untenable economic losses to the grapevine industry worldwide. For example, they are considered a ‘national crisis’ in France, where it has been estimated that 12% of the vineyards are currently economically nonviable due to these maladies causing losses of about €1 billion (Lorch, 2014). Because GTDs are chronic and there is currently no option to eradicate the infections, unproductive vineyards must be replanted, at a worldwide annual cost estimate of €1·132 billion (Hofstetter et al., 2012). The establishment, progress, severity and spread of GTDs have been associated with (i) drought, (ii) limited availability of effective fungicides, (iii) pressure to increase yields, (iv) lack of pruning wound protection due to cost of labour, and/or (v) poor low-quality propagation material. Growers are demanding solutions. Many vineyards with high GTD incidence were planted in the mid-to-late 1990s, during which wine-grape prices were increasing (Volpe et al., 2008). These vineyards were being replanted in the wake of phylloxera, and nurseries could barely meet the demand for grape plants. In 2009 researchers declared the worldwide impact of GTDs and prioritized research goals (Bertsch et al., 2009). Despite scientific advances, there are still unanswered questions, for example about how the pathogens spread and which climate conditions favour infection. This limits the efficacy of GTD management practices. Other crops, such as almond, pistachio and walnut, are now experiencing planting booms (INC, 2015). The significance of these and other tree crops is that they host some of the same GTD pathogens. While researchers fine-tune strategies for managing GTDs in grape, the pathogens may alternate to tree crops, thus endangering the productivity and longevity of orchards and vineyards alike. Recent reports of high disease incidence in pome and stone fruits, nut crops, small fruits and olive in California (Úrbez-Torres et al., 2013), Iran (Mohammadi et al., 2015), Italy (Carlucci et al., 2015), South Africa (Cloete et al., 2011) and Spain (Gramaje et al., 2012) highlight the need for a focus on this new set of hosts. The production systems are different; not all practices for GTD management in grape are feasible in tree crops, especially with dwindling numbers of skilled farm labourers. Tree crop nurseries are struggling to meet demands for plant material and, from our experience with GTDs, a lack of first-quality plants means that low-quality plants are sold, too. Are these plantings doomed to a lifetime of poor productivity? Is the sustainability of the fruit and tree nut industries at risk? Yours sincerely, David Gramaje et al.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it