Most Downloaded Documents for September 2008, University of Nebraska–Lincoln Digital Commons
Bibliographic record
Abstract
The following pages show 2 reports: 1. The 225 documents downloaded 40 or more times during the month of September 2008, by descending order of downloads; with totals for the entire repository. 2. The 105 series with 250+ total downloads during the month of September 2008, by descending order of total downloads. Some highlights: • Total downloads rose by 11,672 (13%) over the total for August, to 104,975. • 14,242 of 18,443 (77%) of available open-access documents were downloaded at least once during the month. The average number of downloads per document was 5.69. • Downloads were furnished to more than 140 countries (data not shown), including Burundi, Mongolia, Rwanda, Uzbekistan, Afghanistan, Nauru, Tuvalu, Angola, Cook Islands, Lao People's Democratic Republic, and The Democratic Republic of the Congo. 36,119 downloads (34% of the total) went to international users. Canada, India, Great Britain, Australia, Germany, France, Italy, Mexico, Spain, and Brazil were the most frequent destinations.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.769 | 0.688 |
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".