ROV basierte Untersuchung der räumlichen \nVariabilität der Lichttransmission durch \narktisches Meereis im Sommer
Bibliographic record
Abstract
The energy balance of the Arctic is of high importance to the climate system of \nour planet also outside the arctic. The sea ice cover as boundary layer between \nocean and atmosphere plays a crucial role in the partitioning of incident energy \nbetween differend compartments of the climate system. While sea ice reflects \nmost of the incident light, some part is penetrating through the ice. Extent, \nthickness and albedo of sea ice have been observed for many years. In contrast, \nthere exist only few measurements of transmitted light, as acces under the ice is \ndifficult. Few data exist especially on the spatial variability of transmitted light. \nThis work presents the first measurements of transmitted light onboard a \nremotely operated vehicle (ROV) under sea ice in the central Arctic. Several \nprofiles of transmitted light were measured during the cruise ARK-XXVI/3 \n(TransArc 2011) of the german research icebreaker Polarstern in 2011 (beginning \nin the eurasian basin, over the pole to the canadian basin and towards russian \nshelf seas). Two spectroradiometers with different angular response characteristics \nwere used for the first time for simultaneous measurements. The amount \nof data enables statistically significant conclusions on a huge databasis and an \napproach to spatial variability of the light conditions under sea ice. Transmittances \nfor different ice types could be derived and the variability within an ice \ntype described. \nOne of the main results of the work is that first year sea ice transmits three \ntimes more light than multi year sea ice. Spatial variability on single floes exceeds \none order of magnitude, but is mainly influenced by surface properties. Due to \nthe lack of a surface scattering layer melt ponds transmit considerably more \nlight than the surrounding bare ice. From the ratio between the two different \nsensors we can deduce, that the scattering-coefficient of sea ice is dependent on \ndirection and that the light field under the ice is not isotropic, as it was assumed \nearlier.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".