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Record W6907431568 · doi:10.21966/9y7d-hb02

Snow Mapping Coastal British Columbia - 2021 - Airborne Coastal Observatory

2021· dataset· en· W6907431568 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHakai Institute · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSnowSnowmeltGlacierPrecipitationClimate changeCryosphereMeltwaterDrainage basinWater cycle

Abstract

fetched live from OpenAlex

Seasonal snow, like glaciers, provides thermal buffering to aquatic ecosystems in alpine and montane environments. The thermal properties of snow also provide a natural phase delay to rivers, supplementing flows during times when precipitation is at a minimum. The economic value of seasonal snow cover is difficult to quantify, but losses under a warming climate could exceed $500B (6). Seasonal snowmelt dominates the hydrology of large river basins in British Columbia (7, 8), yet direct observations of snow depth and snow water equivalent (SWE) in mountain basins are limited; these observations arise from sparse snow observation networks that tend to be centered around infrastructure and people. We thus have little knowledge of the spatial distribution of SWE (eg., SWE as a function of aspect and elevation). Seasonal snow also contributes to the glacier mass balance, so understanding its seasonal fluctuations will be important when assessing where and how quickly glaciers are shrinking. The lack of knowledge about SWE across large areas of the coast, severely limits our ability to calculate the contribution of seasonal snow (and ice) to the annual water budget of rivers, and thus makes it difficult to quantify how climate change will impact future water supply. These limited observations also contribute to a high degree of uncertainty in hydrological models used to predict the hydrological behavior of watersheds both in the past, present and future. In recent years, large scale surveys using airborne laser altimetry (LiDAR) has been applied successfully to watersheds in California (9), and this approach is only starting to be applied in British Columbia, due in large part to the fruitful collaboration between UNBC, Hakai and VIU. Hakai’s Airborne Coastal Observatory was developed to map and monitor icefields to oceans by using a combination of airborne Lidar (Light Detection and Ranging), high-resolution imagery, and hyperspectral imagery. Combined, the ACO sensors provide data to quantify changes in seasonal snow cover and glacier mass loss. The ACO is an aerial remote sensing platform used by the Hakai Institute to survey landscapes in detail. A Piper Navajo aircraft carries an array of integrated airborne mapping sensors installed to collect data in concert. The aircraft is operated and maintained by Kisik Aerial Surveys (Delta, BC).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.015

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.

Opus teacher head0.030
GPT teacher head0.242
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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