Part V, Hemichordata, 2nd Revision, Chapter 1, p. 1-133
Bibliographic record
Abstract
Xylosandrus compactus (Coleoptera: Curculionidade: Scolytinae) is a pest insect with widespread occurrence in 114 countries, causing damage to both seedlings and trees production of approximately 220 species, including Aniba rosaeodora Ducke (Rosewood). In Brazil, X. compactus already occurs in the states of Amazonas, Bahia, Espírito Santo, Minas Gerais, Pará, Rondônia and Tocantins. Due to the difficulty of handling this pest, this study aimed to evaluate the efficiency of the Carvalho-47, IAPAR, and PET-SM traps, using 30 ml of bait, with it being 70° alcohol or a mixture of methanol and 70° alcohol (3:1). The collections were carried out weekly over for 12 months. The experiment was conducted in a Completely Randomized Design (CRD) in a 3 x 2 factorial scheme (3 types of traps x 2 types of bait) with 4 repetitions each. After one year of monitoring, 2.463 individuals were captured, recording reduced losses of 21.42% in the seedling production of A. rosaeodora. There was no statistically significant interaction between traps and baits. The bait composed by methanol plus 70° alcohol was more efficient than the use of 70° alcohol alone. IAPAR and PET-SM trap models were more efficient in capturing X. compactus. Therefore, we recommend these two models, using methanol plus 70° alcohol as bait, to reduce investment costs in ineffective insecticides and avoid losses in seedling production.
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.
How this classification was reachedexpand
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) | 0.042 | 0.053 |
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; both teacher heads agree on what is shown here.
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".