Bibliographic record
Abstract
After 100 years of organized ornithology we have a good picture of the bird fauna of Nebraska. Yes, we will still be able to add new species to the state list, but this will become more and more difficult. What problems are left for the amateur ornithologist to solve? Nebraska is a big state with many different ecological areas, few of which have been studied in detail. Only a few areas of the state have been well studied. Not only do we need data on what species occur, but how many. This type of study can be done by amateur ornithologists. In fact many of the outstanding field studies done in this country and Canada have been done by amateur ornithologists. What does such a study require? A good field study requires a desire to do a good job, good study design, good field notes, and time. By time, I mean that it can't be done in a hurry. A good field study requires several years so that you can get data under various conditions. It is not necessary to spend every day in the field, but spending 12 to 20 days a year in the area will yield good data. I have selected four areas in which over the years I have done fieldwork on insects and ticks [southeast Richardson County, Big Blue Valley (Barneston to Seward), Northern Sioux County (Oglala National Grassland), and Dundy County]. However, these areas would be excellent for ornithological study. The data from such a study would be a major contribution to the ornithology of Nebraska.
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 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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.635 | 0.225 |
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".