Bibliographic record
Abstract
The Pan-American Highway rises in the far north of the Americas at Prudhoe Bay, Alaska and, except for a small gap in Panama, runs the entire length of the two American continents to terminate at Ushuaia in southernmost Argentina. Along its way it travels nearly 50,000 kilometres, from the polar landscape of the far north, through the boreal forests of Canada, the temperate plains and hot deserts of the USA and Mexico, and on further into the tropical zones of Central and South America, until it reaches the sub-polar landscape of Tierra del Fuego. The American landscape was not always like this. To travel along the Pan-American Highway some three million years ago, in the Pliocene Epoch, would have revealed a different world. It was a little warmer than our own. Far away, the Greenland ice sheet covered only a small part of that land mass. At the other end of the world, there was less ice covering the West Antarctic than we are familiar with today. Going south, from Prudhoe Bay along the Pan-American Highway of the Pliocene, there was none of the scrub tundra now seen by the ice road truckers. Forests then extended far to the north, covering vast areas of northern Canada and Alaska, and draping the coastal margins of Greenland. They stretched, too, into Siberia, a mass of forest extending thousands of kilometres from Norway to Kamchatka. There was almost no tundra in the north, except for a few patches in Greenland and on the far northern extremities of Siberia. Instead the polar sun rose across that well-nigh endless green Pliocene forest. Such a prehistoric journey south along the Pan-American Highway would take one across the grasslands of temperate America. These are truly ancient, having been long established even then. Patterns of seasonal temperature and rainfall, though, allowed forests to grow where none are present today. There were no humans to cut down the trees or hunt the animals that lived in the forests. There were no Great Lakes either, for no northern ice had grown yet, to scour out their floors and fill them with melt water.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".