Inferring sedimentary chlorophyll concentrations with reflectance spectroscopy: a novel approach to reconstructing historical changes in the trophic status of mountain lakes
Bibliographic record
Abstract
Reflectance spectroscopy has made it possible to rapidly and nondestructively assess the chlorophyll content of plants and natural waters. However, to date this approach has not been applied to chlorophyll and chlorophyll derivatives preserved in lake sediments. Here, we explore the relationships between visible-near-infrared spectral properties of lake sediments and measured pigment concentrations for lakes that have been exposed recently to anthropogenic nitrogen deposition. Down-core decreases in pigment concentrations and changes in reflectance properties effectively chronicle increases in whole-lake primary production since 1950. Specifically, reflectance spectra of sediments from four alpine lakes in Rocky Mountain National Park (Colorado Front Range, USA) preserve salient troughs near 675 nm that covary in magnitude with concentrations of chlorophyll a and associated pheopigments. The area of the trough in reflectance between 600 and 760 nm best explains the sum of total chlorophyll a and its derivatives (r2 = 0.82, n = 23, P < 0.01). This result suggests that chlorophyll a preserved in lake sediments can be remotely sensed using a simple index derived from reflectance spectroscopy, thus providing a new paleolimnological strategy for rapid exploratory assessments of changing lake trophic status.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".