Bibliographic record
Abstract
The winter Clyde fell through the ice, I was fourteen.We'd had him for several years--Clyde the Jumping Mule, trained to leap fences in pursuit of dogs, who were in pursuit of raccoons, who were running for their lives from Dad's baying hounds.Sometimes he went out on foot with the dogs, but on Clyde, Dad could keep up easier.And the small, bay mule made it easier for Dad to trespass on others' property in the dark of night, too.Fence dividing different parcels of land?Just * * * Skagway didn't look like I imagined, because I didn't know how to imagine any mountain place, having never ventured further west than South Dakota.The town had been a nothing place until the cruise ship industry came in.Just mist and mountains, divided by a long, cold canal out to the ocean.But cruise ships were the reason Mary and I were there.The town grew up around the industry, and its simple, pristine peace was slowly swallowed by guide and tour companies, restaurants, shops and hotels.They lined Skagway's single main street, advertising fresh halibut, fly-fishing expeditions and flight tours, and selling every type of tourist kitsch.Beyond Main Street, though, the bright false storefronts and window displays disappeared.The real Skagway consisted of modest, faded houses on dirt roads.There were trails into the mountains leading to secret lakes and lookouts, and, in the summer, a campground full of vagabonds who served tourists during their shifts at souvenir shops, restaurants and tour companies.Seasonal, poorly-paid squatters from all over the nation.Us.The campsite I shared with Mary was small and semi-hidden in the pines.I pilfered a splintery wooden pallet from behind the Golden North Hotel, Restaurant and Brewery, and a bowed piece of plywood from the hardware store dump (their motto: "If we don't have it, you don't need it").Atop this platform sat my Kelty two-person backpacking tent, purchased from a small outdoor shop in White Horse, Yukon Territory, *
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".