Nuna Nalluyuituq (The Land Remembers): Remembering landscapes and refining methodologies through community‐based remote sensing in the Yukon‐Kuskokwim Delta, Southwest Alaska
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The following article outlines a collaborative, multidisciplinary approach to remote sensing in Southwest Alaska's Yukon‐Kuskokwim (Y‐K) Delta that combines ethnographic inquiry and remote sensing to monitor, detect and preserve cultural resources for Alaskan Native communities. Because distinctive vegetation differences are readily visible on cultural sites during the summer months, the analysis of multispectral imagery obtained from remote sensing is particularly useful. In turn, we demonstrate the efficacy of this protocol on pre‐contact settlement sites along the Ayakulik River system on Kodiak Island using a normalized difference vegetation index (NDVI) raster of the study area. Here, support vector machine (SVM) supervised classification was highly effective at identifying spectral patterns associated with anthropogenic activity while ethnographic data helped rule out false‐positive cases. In addition, we provide the results of a 2019 archaeological prospection survey carried out in conjunction with the ongoing Nunalleq Project in Quinhagak, Alaska, to further highlight the value of ethnographic data collection, ethnobotanical surveys and unmanned aerial vehicle (UAV)‐based spectroscopy alongside SVM supervised classification. Finally, we propose three suggestions for future research on Yup'ik landscapes in the Y‐K Delta regarding citizen science, language preservation and the use of collaborative online maps for community‐based decision making.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it