Introduction From The Editorial Team
Bibliographic record
Abstract
Introduction From The Editorial Team Selima Sultana, Paul Knapp, Ridwaana Allen, and Tyler Mitchell Over a quarter of a century ago, the 1997 SEDAAG meeting held in downtown Birmingham, Alabama, coincided with the second strongest El Niño conditions recorded between 1950 and 2022. Near the conference hotel was a local hardware store that had an impressive display of sleds beneath a sign noting that extreme El Niño conditions suggested cooler (and, by implication, snowier) conditions awaited in the upcoming winter. Great advertising for sure, and an aesthetically pleasing display, but is the presence of El Niño conditions statistically meaningful in the American South? We ask this question since there is a greater than 80 percent chance of El Niño conditions during the fall of 2023 (https://twitter.com/NWS/status/1646510489399336965), and as geographers, we are fascinated with how these events are expressed spatially. So what are temperature conditions like during falls when El Niño conditions prevail in terms of deviations from averages in the American South? The answer is it depends on location. We used the Oceanic Niño Index (ONI) (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php), which measures the three-month running mean of sea surface temperature anomalies in the Nino 3.4 region (5°N–5°S, 120°–170°W). We examined all the years where fall (September through November) conditions met the criteria of El Niño conditions (n = 21) and compared these observations to years when La Niña conditions prevailed (n = 21), which included the falls of 2020–22. We selected Birmingham, Alabama; Greensboro, North Carolina; Norfolk, Virginia (the location of the 2023 SEDAAG meeting); and the Southeast Climate Region, which encompasses much of the SEDAAG Region. Differences between mean annual temperature departures for El Niño and La Niña were small for the selected sites with El Niño years averaging about 1 °F cooler than La Niña years. These differences were significant (p < 0.05) for Greensboro and Norfolk, but not for Birmingham or the Southeastern Region. Additionally, El Niño years are typically associated with below-average temperatures (15 of the 21 years) while La Niña years are more often defined by above-average temperatures (11 of 21 years). In sum, there is an effect, but it is unlikely sled worthy for most of the American South. Our limited analysis helps illustrate the value of viewing these data from a geographical perspective, recognizing that broad-scale assessment based on regions may not capture the intersite variability that exists. Additionally, it is helpful to recognize that even though significant temperature differences exist between phases for some sites, the magnitude of these differences varies substantially between years. Now we will [End Page 226] return to Birmingham and the predicted snowy conditions of 1997–98. Fall conditions were cooler, being -2.1 °F below average, yet winter (December, January, February) conditions were 0.1 °F above average and there were no exceptional snowfall events as that fall/winter do not rank in the top ten of all years (https://www.weather.gov/bmx/climo_snowfacts). It is wonderful to hope for a snowy winter, but the conditions that promote snow events in the American South are regulated by multiple factors that exhibit spatiotemporal variability beyond the effects of El Niño years. Issue 63.3 is comprised of a cover essay about Norfolk and the Tidewater region, four research articles, and two book reviews. The first article of this issue is co-authored by UNC Charlotte geography professor Harrison Campbell, who unexpectedly passed away in October of 2022 during the preparation phase of this manuscript. Since joining UNC Charlotte in 1996, Dr. Campbell made significant contributions to economic geography focusing on the American South. In particular, his research helped us to understand the factors that affect the dynamics of growth, development policy, and the economic well-being of communities and regions. Dr. Campbell was also a longtime and frequent contributor to Southeastern Geographer, either as an author or a reviewer, and was a vocal advocate for SEDAAG. He will be missed dearly by the entire SEDAAG community. As always, this issue was...
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.288 | 0.300 |
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".