1 The trees tell the story Julia Ferguson American Environmental History Fall 2007
Bibliographic record
Abstract
There are very few Americans who know what an untouched landscape looks like. There are trips to the mountains, the north woods, or the national parks and wilderness areas that display some form of that hard-to-find reality, but even these prized American landscapes are for the most part relicts from a past time. What we really see when taking in the view from the trails we are on are landscapes with tumultuous human histories. Rewinding from the present to the past we see sweeping changes happen over short periods of time, creating chapter upon chapter of complicated stories for every square mile of our country. Although very few places are exceptions to this rule, until we learn differently many of us still feel that places we know well and love must be those exceptions: they must be true wildernesses. My family owns a cabin in northern Michigan inside a place called Canada Creek Ranch. When I was little, I imagined it a wilderness, with Chippewa tribesmen stalking quietly through the hummocky cedar swamps in the snow, following ancestors of the deer I watched through the same white-trimmed branches. In the Canada Creek Ranch of my childhood, the trees and woods were ancient and sacred and wild. Sadly, despite the power
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".