An inversion model based on salinity and remote sensing reflectance for estimating the phytoplankton absorption coefficient in the <scp>S</scp>aint <scp>L</scp>awrence <scp>E</scp>stuary
Bibliographic record
Abstract
Abstract The inversion of individual inherent optical properties (IOPs) is very challenging in optically complex waters and within the violet spectral range (i.e., 380–450 nm) due to the strong light attenuation caused by chromophoric dissolved organic matter, nonalgal particulates, and phytoplankton. Here we present a technique to better discriminate light absorption contributions due to phytoplankton based on a hybrid model (QAA‐hybrid) that combines regional Saint Lawrence System estimates of IOPs derived from a quasi‐analytical algorithm (hereafter QAA‐SLE) and empirical relationships between salinity and IOPs. Preliminary results in the Saint Lawrence System during May 2000 and April 2001 showed that QAA‐hybrid estimates of phytoplankton absorption coefficient at 443 nm have a smaller bias with respect to in situ measurements (root‐mean‐square deviation, RMSD = 0.156) than those derived from QAA‐SLE (RMSD = 0.341). These results were valid for surface waters (i.e., 0–5 m depth) of the lower estuary with a salinity and chlorophyll‐a concentration range of 22–28 psu and 2.1–13.8 mg m−3, respectively.
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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 | 0.000 | 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) | 0.001 | 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".