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Record W4252881904 · doi:10.1353/col.2012.0071

Weisshorn

2012· article· en· W4252881904 on OpenAlexaboutno aff
Heidi Diehl

Bibliographic record

VenueColorado review · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEveningArtArt historyHistoryPhysics

Abstract

fetched live from OpenAlex

59 HEIDI DIEHL WEISSHORN I n the hotel’s dining room, a glass punch bowl of pink yogurt sat higher than everything else on the table, pedestaled above cornflakes and muesli, small sausages, cheese rinded with what looked like shreds of grass. Coffee asserted itself. There were croissants lined up and rolls in a basket. I didn’t know where to begin. “There she is.” My mother was suddenly beside me, her face radiant. Her long gray hair was wrapped in a bun above her turtleneck. For years, she’d refused to cut her hair short; she didn’t want to look like an old lady. Until this trip, I hadn’t thought of her that way—she was seventy-two, but still working as an archivist, still hiking and taking tai chi classes at the Y. But when I hugged her beside the yogurt, she felt insubstantial in my arms, her shoulders narrow, folding together. “We thought you would be here yesterday evening,” she said. I was joining my mother and her husband Werner’s family in the Alps to celebrate Werner’s seventy-fifth birthday. The trip from New York, on two flights and three trains, had taken me over twenty-four hours. “An old man started having breathing problems on the plane. We had to land on an airstrip in Newfoundland.” I’d been delayed by the emergency landing; my late arrival in Zurich made me miss my train connections, and I’d finally gone up into the mountains in complete darkness. I made it to the hotel after midnight, and I hadn’t been able to tell anyone this story yet. “They didn’t give us any information—just told us to fasten our seatbelts. I thought we were going down in the ocean.” “Come and see everyone, Nina,” my mother said. She linked her arm through mine and led me to a round table beside huge windows. The view was stunning—mountains beyond the green and white of fir trees and snow, everything perfect through the glass. colorado review 60 Werner stood up to greet me. “I’m sorry I was so late,” I said as I embraced him. “I hope you weren’t worried.” “Oh, no,” Werner said with his usual briskness. “We knew you’d show up. Here is Ulf.” Werner motioned to his tanned and well-preserved brother, who greeted me warmly, though I’d met him only a handful of times, the first when my mother married Werner on a festive night twelve years ago. The brothers were Swiss, though Werner had lived in the u.s. for thirty years, studying caribou migration and teaching wildlife biology. Werner continued his introductions. “Rudi and Elke,” he said, motioning to a couple around my age wearing matching fleece vests—Werner’s son and his wife. They lived in Basel and worked for Ulf. I’d never met Elke, and I barely knew Rudi. He squeezed my hand. He didn’t look like Werner, who was wiry, who’d been dark-haired before his beard turned white. Rudi was strapping and blond, with the shaggy affability of a golden retriever. I returned to the breakfast spread and filled a small plate, avoiding the strange cold cuts, gravitating toward the cheese. Back at the table, I sat next to my mother. “Our latest American representative,” Rudi said to me. He cut his roll with a knife and fork. “What do you think of our mountains?” “I’m glad to see them in daylight,” I said. “How is the view?” Elke asked. Her crisp accent made her question almost accusatory. “Did you see the Weisshorn?” She buttered a croissant, which seemed like overkill to me. “It’s beautiful.” I’d seen many mountains—one of them must have been the Weisshorn. “I looked at it from my bed this morning .” “For me, the same. I awoke to something gorgeous, and I’m I wanted to talk to my mother , to see if I could notice any differences. She seemed to be listening with her usual sharp focus, but I was afraid of what I might see if I looked closely. } 61 Diehl not referring to my...

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.392
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.6080.440

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.

Opus teacher head0.087
GPT teacher head0.244
Teacher spread0.157 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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