A new index for the rapid generation of chlorophyll time series from hyperspectral imaging of sediment cores
Bibliographic record
Abstract
Abstract Time series analyses of pigment concentrations are key to understanding past aquatic ecosystem dynamics. As lake sediments provide a window into longer‐term changes, innovative paleolimnological chlorophyll quantification could provide impactful insights into past environmental processes. Lab‐based hyperspectral imaging of sediment cores is an emerging technique to develop rapid, non‐destructive, high‐resolution chlorophyll inferences but it requires more extensive vetting. Despite recent advances in model development, there is still a knowledge gap about the reliability of chlorophyll models when applied in lakes with diverse properties, as well as the potential confounding effects of physical sediment properties on these models. We assessed the performance of 23 chlorophyll indices based on paired measurements collected via hyperspectral imaging and spectrophotometry for 202 samples spread across seven Canadian lake sediment cores. The best performance was by a new index based on the wavelength of the red‐edge minimum point (λREMP). We tested the applicability of λREMP to a broad range of sediment cores using a database of 116 cores, and found the index to provide reliable reconstructions of ƩChl (i.e., chlorophyll a and b and their degradation products) trends in 84% of sites. Further analyses indicated that sediment characteristics including particle size, organic matter content, water content, and density had no systematic impact on ƩChl, but greater sediment brightness did increase ƩChl inferences from hyperspectral images. Hyperspectral core scanning is poised to facilitate the generation of high‐resolution chlorophyll time series data, which could greatly improve our understanding of trajectories of change from the local to global scales.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".