Bibliographic record
Abstract
The Winter of the Future?December: Temperatures averaged 27.4, 3.7 below normal.The high was 51 on the 28 th and the low was 1 on the 8 th .Lake Erie water stood at 44 on the 1st and fell to 34 by the 31 st .Snowfall totaled 21.0 inches and the greatest depth was 8.0 inches on the 13 th .Precipitation was distributed over 16 days, totaling 2.04 inches, 1.10 inches below normal.The greatest fall in any 24 hour period was 0.29 inches on the 1 st and 2 nd .January: Temperatures averaged 38.6, 12.9 above normal.This was the third warmest January on record for Cleveland.The high was 60 on the 13 th and the low was 21 on the 27 th .Lake Erie was at 34 by the end of the month.Snowfall totaled only 4.6 inches while the greatest ground depth was 1 inch on the 18 th and 19 th .Precipitation was distributed over 15 days to total 1.92 inches, 0.56 inches below normal.The greatest fall in any 24 hour period was 0.61 inches on the 17 th and the 18 th .February: Temperatures averaged 30.5, 2.1 above normal.The high was 61 on the 16 th and 17 th and the low 5 on the 19 th .Lake Erie dropped to 33 by the end the month.Snowfall totaled 16.9 inches, with the greatest depth 8 inches on the 9 th .Precipitation totaled 2.80 inches, .51inches above normal and occurred over 15 days.The greatest 24 hour fall was .84inches on the 4 th and 5th.Ross's Goose -One was found 9 January along Wolf Road in Bay Village, Cuyahoga Co. (LRi, CC, BD).A first winter plumaged white morph was at Wellington Reservoir, Lorain Co. 28 -29 January (DJH, SS).Cackling Goose -Two were reported from a field north of Perry Schools, Lake Co. 3 January (JP).This species, once considered a subspecies of Canada Goose, requires documentation sent to the Ohio Bird Records Committee. Canada Goose -Wintering birds areUnderline denotes unexpected species in the region HBSNP -Headlands Beach State Nature Preserve
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.878 | 0.811 |
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".