Bibliographic record
Abstract
In 1811, Henry Marie Brackenridge walked up the Cahokia Creek in search of ancient mounds. Brackenridge lived in St. Louis – a town known for years as Mound City – and had been told that the biggest mounds in the area could be found on the grounds of a Trappist monastery on the other side of the Mississippi River. After walking for about four miles into the floodplain, he found himself in front of the largest earthen mound ever constructed north of Mexico (Young and Fowler 2000: 3–5). Two years later, he described this moment in a letter to his good friend, the former president of the United States Thomas Jefferson (1962 [1814]: 187): When I reached the foot of the principal mound, I was struck with a degree of astonishment, not unlike that which is experienced in contemplating the Egyptian pyramids. What a stupendous pile of earth! To heap up such a mass must have required years, and the labor of thousands. Brackenridge spent the remainder of that day exploring the dozens of mounds that made up the site. He found flint, animal bones, and pottery littering the surface and concluded that “if the city of Philadelphia and its environs were deserted there would not be more numerous traces of human existence” (1962 [1814]: 186). Henry Marie Brackenridge was perhaps the first European American to recognize the significance of Cahokia and its related sites in the St. Louis area.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.018 |
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".