Bibliographic record
Abstract
Abstract This chapter considers advances in geomorphological modelling from 1965 to 2000, a period that coincides with enormous progress resulting from the digital revolution. The status of computer technology and the discipline of geomorphology leading up to the period covered in this chapter is outlined. The emergence of electronic, digital computers was a significant development in computing history. The invention of the microchip resulted in a generation of mainframe computers that were increasingly efficient and powerful. Throughout the 1960s, universities and research organizations acquired mainframe computers, with time-sharing replacing batch processing. Until the mid-twentieth century, geomorphological research focused on qualitative studies of landscape history. Thereafter, geomorphological research focused on process-based studies over smaller scales. At the time that mainframe computers allowed for complexity in mathematical modelling, geomorphologists began to concentrate on an agenda that did not necessitate their use. Some mathematical models in geomorphology were introduced between 1965 and 1980, although equations were often solved analytically. Despite accessibility to powerful computers in the 1980s, mathematical modelling in geomorphology was not widespread. Mathematical models in geomorphology appeared with increasing frequency in the 1990s. Disciplinary engagement with computationally intensive approaches was strong during this decade, and continued into the twenty-first century.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".