Bibliographic record
Abstract
By Craig CaldwellTemperatures in Mar were below average, with the statewide mean, high, and low ranging between the 25th and 35th percentiles.Statewide, precipitation was a bit above average but varied considerably.The southern quarter of the state received 150 to 200% of its usual amount and the middle half between 90 and 200%, but the northwest and lakeshore received from less than half to about 90% of their norms.In Apr, the average, high, and low temperatures statewide were all in the upper third of the 121 years with data but did not break into the highest 20%.Precipitation was above average everywhere but the northwest corner, which received less than 90% of its norm.Rainfall in the rest of the state was up to double its usual amount except for the Portsmouth area, which was soaked with three to four times its average.May was among our hottest ever.The average temperature was our 11th highest, part of a heat wave which affected the entire northeast quadrant of the country and set many records in New England.The statewide average minimum and maximum followed suit; they were our 14th and ninth highest, respectively.Cleveland and Akron tied or set several record high temperatures between 07 and 09 May.The lower than average rainfall overall brought no relief.The southern half of the state received from less than 25% of its norm to only about 90%.Paradoxically, most of the northern half received between 90 and 150% of its average rainfall and small areas in the northeast and northwest were drenched with up to triple their usual amount.Weather data are from the National Weather Service (http://water.weather.gov/precip/),the National Oceanic and Atmospheric Administration (http://www.ncdc.noaa.gov/temp-and-precip/maps.php and http://www.ncdc.noaa.gov/extremes/records/), and the Plain Dealer.Andy Jones posed an interesting question: Are we seeing more American White Pelicans because they are more common than 20 years ago, or because there are more observers?He suspected the latter, which I agree is likely a part of the answer.However, we found that eBird data show larger numbers are wintering further north along the Atlantic coast than before, which could contribute to more pelicans crossing Ohio on the way to their nesting areas in the middle of the continent.Granted, eBird usage is also growing, so the "more observers" phenomenon may also
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.170 | 0.099 |
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".