Bibliographic record
Abstract
Sitting atop a rickety Indian bus trundling up the Beas Valley to the delightful hill station of Manali in the Kulu Valley of Himachal Pradesh, peering between swaying bodies and piles of luggage, I caught my first glimpse of the snowy peaks of the Himalaya. Yes, those white streaks way up in the sky were not, after all, clouds: they were glints of sunlight on impossibly high and steep ice-fields plastered onto the sides of mountains that tore up into the sky. It was a sight to take one’s breath away and I knew instantly that this was going to be the start of a great adventure. We were a typical shoestring British student expedition of five friends who could fit easily into two overloaded rickshaws, heading for the mountains around the Tos Glacier. Mountaineers dream about climbing in the Himalaya. Since my earliest days of climbing the hills and crags of Snowdonia and northern Scotland, I had yearned to see and climb those magical Himalayan Mountains. Now here I was, and the reality of the Himalaya was even better than I imagined. I had taken three months off from my PhD studies on the geology of the Oman Mountains to go on this expedition. We had driven a Land Rover out from England to Muscat through a snowy Europe and across the Empty Quarter of Arabia from Syria and Jordan to the United Arab Emirates and Oman. After three months’ fieldwork in Oman I caught a passenger ship, the MV Dwarka, last of the British East India Company merchant vessels that plied the Gulf route from Basra via Kuwait, Bahrain, Dubai, and Muscat to Karachi, and then travelled through Pakistan by train into India. That first expedition to Kulu was a revelation. We camped on the Tos Glacier, four days’ walk above the village of Manikarin in the Parbati Valley of eastern Kulu, for about four weeks. During that time the weather was perfect almost every day.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.014 |
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".