Trend 09/27/2010 - 01/14/2017. National Oceanic and Atmospheric Administration. Global Historical Climatology Network - Daily: US Only: Precipitation (Total) | Country: USA | State: New Jersey | Weather Station: NEW BRUNSWICK 0.3 NE, 09/27/2010-01/14/2017. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 018-005-041.
Bibliographic record
Abstract
National Oceanic and Atmospheric Administration (2017). Global Historical Climatology Network - Daily: US Only: Precipitation (Total) | Country: USA | State: New Jersey | Weather Station: NEW BRUNSWICK 0.3 NE, . Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 018-005-041. Dataset: Reports total precipitation recorded at weather stations in the United States. GHCN (Global Historical Climatology Network)- Daily (Version 3.22) is an integrated database of daily climate summaries from land surface stations across the globe. The data are obtained from numerous sources and integrated and subjected to a common suite of quality assurance reviews. The data provided here are from the subset of United States weather stations participating in the GHCN. Numerous daily variables are provided, including maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about two-thirds of the stations report precipitation only. Both the record length and period of record vary by station and cover intervals ranging from less than one year to more than 175 years. From the technical documentation: "The dataset cannot be used to quantify all aspects of climate variability and change without any additional processing. In general, the stations providing daily observations were not managed to meet the desired standards for climate monitoring. Rather, the stations were deployed to meet the demands of agriculture, hydrology, weather forecasting, aviation, etc. GHCN-Daily data have not been homogenized to account for the potential artifacts associated with the various reporting practices at stations. Users must consider whether the potential for changes in systematic bias might be important for their particular application." Category: Natural Resources and Environment Source: National Oceanic and Atmospheric Administration The National Oceanic and Atmospheric Administration (NOAA), within the United States Department of Commerce, was formed by Reorganization Plan No. 4 of 1970. Its mission is to monitor and assess the state of the environment in order to make accurate and timely forecasts to protect life, property, and natural resources, as well as to promote the economic well-being of the US and to enhance its environmental security. The National Climatic Data Service within NOAA provides weather, water, and climate warnings, forecast, and data for the US and adjacent waters. http://www.noaa.gov/ Subject: Weather, Precipitation
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.138 | 0.186 |
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".