Kinguneq Ciunerkiurluku: Nunalget Elakengaliuryarait, Ayagyuat Ilagauciat, Paitait-llu Kuinerrami Alaska-mi
Bibliographic record
Abstract
Kuinerrarmiut kinguvrita ayagniuskiit Tegganrita-llu cingumakiit Nunallermek Elakengaliuryaranek Caliarat [Nunalleq Archaeology Project] Yup’igni Alaska-rmiuni ayuqaitellruuq ayagniatni 2009-aami. Tuakenirnek nakmiin nunameggni caliamegteggun qulen allrakut cipluki elakengaliuryararluteng elakengengnaqellrianek, elakenganek yuvrilrianek, kangingnaurvigteggun qelkilrianek, 2018-aarnirnek-llu nunameggni elakengellmeng tamalkuita qellekviatnek. Elakengaliurtet Nutemllaat-llu Kinguvrita caliaritnek qanemcini Tegganret kinguvarturtet-llu arcaqaketuit. Tau͡gaam tamakut calillgutkuciat man’a engelkarrluku arcaqaqapigcaaqengraan Nunallermek caliaratnun, makuni eneqakaput ayagyuat anglillret Caliarat maliggluku, maa-i-llu elakengat tapeqluki paitaqsagutellruluki. Makuni igani qalarutkaput Nunallermek Elakengaliuryaranek Caliarata agtuumaciat nunalget paitaitnun, mumiggluku-llu nunalget ilagautellermegteggun elakengaliuryaranun agtuumaciat, atunem-llu yugnun paivtellerkiullrat. Qulen allrakut cipluki iluatni murilkelput umyuangcautekenqegcaarluki, qaillun-llu tamakucit nunalgutkellriit-llu calillguteksarait kinguvqaarni elluarcaryugngaciatnek.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.098 | 0.016 |
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".