Light absorption and partitioning in Arctic Ocean surface waters: impact of multi year ice melting
Bibliographic record
Abstract
Abstract. Ice melting in the Arctic Ocean exposes the surface water to more radiative energy with poorly understood effects on photo-biogeochemical processes and heat deposition in the upper ocean. In August 2009, we documented the vertical variability of light absorbing components at 37 stations located in the southeastern Beaufort Sea including both Mackenzie river-influenced waters and polar mixed layer waters. We found that melting multi-year ice released significant amount of non-algal particulates (NAP) near the sea surface relative to sub-surface waters. NAP absorption coefficients at 440 nm (aNAP(440)) immediately below the sea surface (0-) were on average 3-fold (up to 10-fold) higher compared to sub-surface values measured at 2–3 m depth. The impact of this unusual feature on the light transmission and remote sensing reflectance (Rrs) was further examined using a radiative transfer model. A 10-fold particle enrichment homogeneously distributed in the first meter of the water column slightly reduced photosynthetically available and usable radiation (PAR and PUR) by ~6% and ~8%, respectively, relative to a fully homogenous water column with low particles concentration. In terms of Rrs, the particle enrichment significantly flattered the spectrum by reducing the Rrs by up to 20% in the blue-green spectral region (400–550 nm). These results highlight the impact of melt water on the concentration of particles at sea surface, and the need for considering nonuniform vertical distribution of particles in such systems when interpreting remotely sensed ocean color. Spectral slope of aNAP spectra calculated in the UV domain decreased with depth suggesting that this parameter is sensitive to detritus composition and/or diagenesis state (e.g., POM photobleaching).
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.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".