Spatial and temporal characteristics of past droughts in <scp>New Brunswick</scp> (1971–2020)
Bibliographic record
Abstract
Abstract This study aimed to provide a historical picture from 1971 to 2020 of the state of droughts in New Brunswick (NB). More specifically, we wished to determine whether there were statistically significant trends using two drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation‐Evapotranspiration Index (SPEI). Monthly precipitation (rain and snow) and average maximum and minimum temperatures were obtained from 10 stations located in New Brunswick. Then, we applied Mann–Kendall and Sen's Slope's nonparametric tests to these two indices at a 1‐, 3‐, 6‐ and 12‐month timescale. The results have shown that the average value for the SPI and SPEI evolution throughout the study period indicated a decrease in indices values (at 95% confidence level). The temporal evolution of the SPI and SPEI were similar at various timescales regarding severities and intensities. However, there were still slight differences in the fluctuation values, especially during the last 20 years of the study period. At a 12‐month timescale, both indices indicated the driest years as being 2020, 2001 and 2004. The SPEI Miramichi, Saint John and Moncton stations had maximum negative MK values (−0.259, −0.221 and −0.215), respectively. As for spatial distribution, four stations located in the south stood out with the highest increase in dry conditions and significantly negative trends (meaning increased drought in this case). Stations in the northern part of the province (Bathurst and Charlo) show non‐significant trends. This study suggests a slight drought increase trend exists in New Brunswick, while an increase in annual precipitation is expected between 42 and 324 mm by 2100 according to climate scenario RCP8.5 of CMIP6.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".