Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice
Bibliographic record
Abstract
Included are the data presented in the publication entitled: Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice accepted for publication in The Cryosphere Journal (2021). The data set includes Data and codes: 1. Data (duplicated in .xlsx and .mat): 1.1 Sites coordinates- (figure 5) -Geolocalisation of both sea ice sampling sites visited for this study (1 and 4) 1.2 cumu_sg- (figure 6)- cumulative signal vs depth vs source-detector distance vs scattering coefficient obtained with Monte Carlo simulations —cumu_sg- cumulative signal (%) — depth (mm) —standard deviation on depth where signal is cumulated —ddet (mm)- radial distance between source and detection point — b (m^-1)-scattering coefficient 1.3 validation-(figure 7)- Error on IOPs vs IOP value estimated measuring on microspheres solutions —vf (-)- microspheres volume fraction (in water) —a_theo (m^-1) - theoretical value of the absorption coefficient — mean_error_a(%) - error between theoretical value and measured value —std_error_a_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter) —std_error_a_y (%) -standard deviation on error_a —rb_theo (m^-1) - theoretical value of the reduced scattering coefficient —mean_error_rb(%) - error between theoretical value and measured value —std_error_rb_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter) —std_error_rb_y (%)) -standard deviation on error_rb —gamma_theo (-) - theoretical value of gamma —mean_error_gamma (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter) —std_error_gamma (%) - standard deviation on error_gamma -1.4 T-S-(figure 8)- Vertical profiles of temperature and bulk salinity of sampled sea ice available at both snow covered site 1 and bare ice site 4 —T (celsius) - ice temperature —S_si (ppt) - ice bulk salinity —depth (cm) 1.5 Rmes-(figure 9)-Vertical profiles of spatially resolved diffuse Reflectance in sea ice using different covers to shade available at both snow covered site 1 and bare ice site 4 —Rmes (-) - spatially resolved diffuse Reflectance —Rmes_nbg (-) - spatially resolved diffuse Reflectance with no background sunlight subtraction in calculation of Rmes —dmes (mm) - distance between source and detecting fibre (named rho in the paper) —depth (cm) — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp 1.6 IOPprofiles-(figure 9)-Vertical profiles of reduced scattering coefficient in sea ice using different covers to shade available at both snow covered site 1 (ice+snow) and bare ice site 4 —infferedrb (m^-1) - reduced scattering coefficient —infferedrb_nbg (m^-1) - reduced scattering coefficient with no background sunlight subtraction in calculation of Rmes —cr1 (binary)— criteria determining if the measurement is kept or not —depth (cm)- depth from the surface . **watch out** at site 1 , the measurments start from the surface of the snow. Substract 24 cm to get measurement from surface of the ice. — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp 2. Code (written in .m with MATLAB_R2018b ®) : 2.1 inversion algorithm—(figure 9 ) — used to find rb from Rmes (dmes) vertical profiles in sea ice — Main_vprofiles_Rtorb-qik2019_article.m - Main script of the inversion alorithm to get rb from Rmes (dmes) —importfiledata.m-subfunction to import data from .csv —importfiledatamay8.m-subfunction to import data from .csv (specific to may 8th because file was corrupted) —interp1lookup_HR_enlarged_bin10.mat - lookup table of Reflectance vs dmes vs a vs b’ vs gamma used in the inversion —calibjune6_ha_interp1_indcalib2.mat - calibration factor with microspheres as a reference —site1_c20-picture of the ice core taken at site 1 —site4_c20-picture of the ice core taken at site 4 —may8th+othertests_fixed.csv-raw data from may 8 (site1) —may9day3.csv-raw data from may 9 (site4)
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".