Bibliographic record
Abstract
First posted July 19, 2022 For additional information, contact: Program Coordinator, National Land Imaging Program U.S. Geological Survey 12201 Sunrise Valley Drive Reston, VA 20192Contact Pubs Warehouse The State of North Dakota once did not figure prominently in the Nation’s economy. The sparsely populated State supported food production, and hunters and anglers were drawn to its lakes, rivers, and wide-open spaces, but its economy was overshadowed by that of other States. However, the State and its prairie expanses recently rocketed from an economic afterthought to a national energy leader with the soaring production of oil and natural gas in the Bakken oil patch.The Bakken development has been transformative for North Dakota’s landscapes in myriad ways. It has boosted economic output, drawn thousands of new residents to cities like Williston and Watford City, and led to a proliferation of oil and gas pads.In the past two decades, North Dakota experienced other major changes, such as the expansion of the depressional wetlands of the Prairie Pothole Region on the eastern side of the State. These critical breeding areas for waterfowl, which stretch across Minnesota, South Dakota, North Dakota, and Canada, are home to more than 50 percent of North America’s migratory birds.Changes from oil and gas production, urban development, and wetland resurgence can all be tracked over time using the unparalleled Earth observation record of the U.S. Geological Survey Landsat data archive. Its 50-year record of repeat imagery also aids in the monitoring, cataloging, and management of cropland, invasive insect species, and natural or human-made disaster recovery. Here are just a few examples of the benefits offered to North Dakota by the Landsat Program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".