Unique insights into firn structure across western Greenland’s percolation zone from hyperspectral images of shallow firn cores
Bibliographic record
Abstract
The physical structure of the firn column directly influences the transport and storage of infiltrating water generated by surface melt in ice sheet accumulation zones. Firn density is relatively easy to measure in field or laboratory settings and provides porosity-based estimates of the meltwater storage capacity but does not describe meltwater movement through open pore space. Pore structure controls meltwater flow and is better characterized by microstructural parameters, such as grain size. Firn grain size is therefore a state variable that needs to be accurately modeled or measured to quantify meltwater transport and storage in the firn column. Manually or digitally measuring grain size from firn samples can be tedious, time consuming, and subjective. Here, we characterize firn structure from 14 firn cores spanning approximately 1000 km across western Greenland’s percolation zone. We scanned the top 10 m of each core with a near infrared hyperspectral imager (NIR-HSI; 900-1700 nm) mounted on a linear translation stage. Leveraging the relationship between ice grain size and near infrared absorption, we invert measured reflectance to retrieve an effective grain radius, resulting in a high-resolution (~ 0.4 mm) grain size map of the firn core. We compare the retrievals against traditional grain size measurements from 7 of the cores. Additionally, the hyperspectral firn core grain size maps allow for quickly retrieving vertical ice layer distributions within the firn column and identifying regions that have been previously wetted that are not readily apparent by visual inspection. We use our unique dataset to examine correlations between grain size, infiltration ice content, and measured firn density to determine whether microstructural information can be extracted from firn density measurements. While cores provide a snapshot of firn conditions at the time of collection, we show that hyperspectral imaging of firn cores can reveal a detailed hydrologic history of the firn column and provide validation data for modeling future meltwater percolation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".