Determining the influence of Itaipu Lake on thermal conditions for soybean development in adjacent lands
Bibliographic record
Abstract
Previous numerical simulations have suggested that the area adjacent to Itaipu Lake in Southern Brazil is significantly affecting the local thermal regime through development of a lake breeze. This has led to concerns that soybean growth and development, and consequently yield, has been affected by the creation of the artificial lake in this important agricultural region, but a systematic climatological study of the thermal effects of Itaipu Lake has not been conducted. The objectives of this study were to assess the spatial pattern of minimum and maximum air temperatures in a 10-km-wide area adjacent to Itaipu Lake as affected by distance from the water. Measurements were conducted over 3 years in seven transects along the shore of Itaipu Lake, with five weather stations placed in each transect. Phenological observations in soybean fields surrounding the weather stations were also conducted. Generalized additive models for location, scale, and shape (GAMLSS) analysis indicated no difference in the temperature time series as distance from water increased. Semivariograms showed that the random components in the air temperature were predominant and that there was no spatial structure to the signal. Wind direction measured over the three growing seasons demonstrated that, on average, the development of a lake breeze is limited to a few locations and a few hours of the day, supporting the temporal and spatial analysis. Phenological observations did not show differences in the timing of critical soybean stages. We suggest that the concerns that soybean development is potentially affected by the presence of Itaipu Lake are not supported by the thermal environment observed.
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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) | 0.000 | 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 teacher head, 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".