Coloured dissolved organic matter absorption of unfiltered samples from sea ice cores from the coastal zone of the southern Canadian Beaufort Sea, near Herschel Island - Qikiqtaruk
Bibliographic record
Abstract
For DOC and CDOM absorption, samples were filtered through a 0.7 μm glass fibre filter (Whatman GF/F syringe filter) which had been rinsed with 20 mL sample water. DOC samples were collected in 20 ml glass vials with septum lid, acifdified with HCl to pH < 2 and stored at 4°C until analysis. DOC was measured with a Shimadzu TOC-V analyzer. CDOM samples were collected in 100 mL amber glass bottles that were stored in the dark at 4°C until analysis. aCDOM was measured at the German Research Center for Geosciences (GFZ), Potsdam, Germany using a double beam LAMBDA 950 UV/Vis (PerkinElmer) spectrophotometer. The absorbance (A) was measured between 200 and 800 nm in 1 nm steps using a 5 cm cuvette. Absorption (a) was calculated from the resulting absorbance measurements via aCDOM(λ) = 2.303 * A(λ) / l, where l is the path length (length of cuvette in meter). Every 5 to 10 samples, the reference sample (Milli-Q water) was exchanged and a blank was measured to avoid instrument drift. Spectral slopes (S275-295, S350-400) as well as the Slope Ratio (SR = S275-295/S350-400) were derived using the linear regression slope of the log-transformed (natural logarithm) absorption spectra (Helms et al. (2008). Specific UV absorbance (SUVA) is defined as the UV absorbance of a water sample at a given wavelength normalized for dissolved organic carbon (DOC) concentration. SUVA254 is defined as the UV absorption at 254 nm divided by the DOC concentration measured in mg L-1 (Weishaar et al., 2003). Spectral slope, Slope Ratio and SUVA were only calculated for data from filtered samples as these parameters purely depend on dissolved matter properties. CDOM absorption spectra from unfiltered aliquots of the same sample might still be of interest as they provide insight into bulk optical properties, which are crucial for total absorption budgets and remote sensing algorithms that do not distinguish dissolved vs particulate absorption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".