Spectral albedo measurements of the sea ice surface during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign in the Central Arctic Ocean, April – September 2020
Why this work is in the frame
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Bibliographic record
Abstract
This dataset contains spectral albedo data recorded on the sea ice surface June-September, 2020, during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition expedition in the Central Arctic Ocean. Measurements were made in three modes: (i) along ‘albedo lines’, between 60-200 meters (m) in length, with measurements every 5 meters, (ii) at specific ‘library sites,’ or (iii) ‘experiments’. Albedo lines were chosen with the aim of crossing representative surface conditions during the summer sea ice evolution, including snow-covered ridges, bare ice, and ponded ice. Included in the dataset are classification of the surface cover and depth for most measurements. Spectral albedo data was collected using an Analytical Spectral Devices (ASD) FieldSpec Pro spectroradio meter with a custom spectralon cosine collector. Incident and reflected values were recorded subsequently, with 10 scans averaged for each.Processing of the data includes calculating an albedo from the relative values of incident and reflected scans, and completing quality control to (i) correct for parabolic offset between sensors, (ii) add flag quantifying variability of incident light that may be used to filter scans, (iii) remove scans with physically unrealistic values or slopes, and (iv) remove and filter noisy parts of the spectrum. This dataset is collocated with the broadband albedo dataset (doi.org/10.18739/A2KK94D36) and albedo photo dataset (doi.org/10.18739/A2B27PS3N).
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 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| 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