Cross‐correlation patterns of air and soil temperatures, rainfall and <i>Diaprepes abbreviatus</i> root weevil in citrus
Bibliographic record
Abstract
Time series cross-correlation analysis is appropriate when measuring relationships between two different time series. Using this approach, the authors quantified the relationship between the time series air temperature (AT), soil temperature (ST), rainfall, relative humidity (RH) and Diaprepes abbreviatus (L.) (Coleoptera: Curculionidae) root weevil across a period of 30 months, and examined how closely the distribution of Diaprepes root weevil was related to AT, ST, rainfall and RH within this period of time. The study was conducted on a poorly drained Spodosol in a citrus [Citrus sinensis (L.) Osb.] grove in DeSoto County, south-west Florida, from April 2001 to September 2003. Adult weevil populations were monitored using 100 Tedders traps in a 30 x 15 m grid. Weather data (0.6 m AT, 0.1 m ST, 2 m rainfall and 2 m RH) were monitored by Florida Automated Weather Networks. The monthly mean and standard deviation were 22.3 +/- 4.0 degrees C for AT, 24.7 +/- 4.2 degrees C for ST, 146.0 +/- 122.7 mm for rainfall, 78.2 +/- 4.7% for RH and 0.74 +/- 0.59 adults trap(-1) for the root weevil. Weevil density was positively correlated with AT (r = 0.45, P < 0.0133), ST (r = 0.49, P < 0.0067) and rainfall (r = 0.38, P < 0.0450). The environmental variables AT, ST, rainfall and RH were correlated with each other (0.42 < r < 0.99, 0.0246 < P < 0.0001). All weather and Diaprepes variables were autocorrelated with each other within a time of 3 months. The cross-correlation coefficients varied between - 0.59 and 0.65 for the pair-variable between Diaprepes, AT, ST and rainfall, and these pair-variables were correlated across a time period of 4 months. The present results suggested that warm, wet conditions contributed to the root weevil outbreaks, and environmental temperature and rainfall were the variables most closely related to Diaprepes root weevil distribution in time.
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 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 | 0.001 |
| 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) | 0.000 | 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".