Vertebrate species richness data from 7 sampling sites along the Yukon River in Alaska during the summer of 2012
Bibliographic record
Abstract
This data set describes results from a species richness project conducted in Alaska during May-July, 2012. Seven locations were surveyed for 5 consecutive days each to detect the presence of any vertebrate species occurring there. A variety of methods were employed including trapping, point counts, and casual observation. Sampling locations were selected on the basis of representative sampling of biomes and a range of elevations along the Yukon River in Alaska. The following sites are included: Yukon Crossing (latitude 65.86579 N, longitude -149.74113 W, elevation 200 m), Kokrine Hills (latitude 64.92532 N, longitude -154.99438 W, elevation 730), Nulato Hills (latitude 64.44390 N, longitude -150.60069, elevation 28), Fox Point Island (latitude 63.02524 N, longitude -159.79390 W, elevation 25), Kako Mountain (latitude 61.91278 N, longitude -159.45473 W, elevation 196 m), Mountain Village (latitude 62.11773 N, longitude -163.84276 W, elevation 7 m), Bethel (latitude 60.81123 N, longitude -161.81007, elevation 14). Species were described with the Integrated Taxonomic Information System (ITIS.org). For wider context and information, please see also related datasets for plot descriptions, small mammal trapping, plant collections and a plot-specific species list. This dataset is part of an ongoing PhD thesis by the author.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".