Reconstructing historical time-series of cyanobacteria in lake sediments: Integrating technological innovation to enhance cyanobacterial management
Bibliographic record
Abstract
The global rise of cyanobacterial blooms emphasizes the need to develop tools to manage water bodies prone to cyanobacterial dominance. Reconstructing cyanobacterial baselines and identifying environmental drivers that favour cyanobacterial dominance are important to guide management decisions. Conventional techniques for estimating cyanobacteria in lake sediments require considerable resources, creating a barrier to routine reconstructions of cyanobacterial time-series. Here, we compare a relatively simple technique based on spectral inferences of cyanobacteria using visible near-infrared reflectance spectroscopy (VNIRS) with a molecular technique based on real-time PCR quantification (qPCR) of the 16S rRNA gene conserved in cyanobacteria in 30 lakes across a broad geographic gradient. We examined the sedimentary record from two perspectives: 1) relationships throughout the entire core (without radiometric dating); 2) relationships post-1900s with the aid of radiometric dating (i.e., 210Pb). Our findings suggest that the VNIRS-based cyanobacteria technique is best suited for reconstructing cyanobacterial abundance in recent decades (i.e., circa 1990 onwards). The VNIRS-based cyanobacteria technique showed agreement with those generated using qPCR, with 23 (76%) lakes showing a strong or very strong positive relationship between the results of the two techniques. However, five (17%) lakes showed negligible relationships, suggesting cyanobacteria VNIRS requires further refinement to understand where VNIRS is unsuitable. This knowledge will help scientists and lake managers select alternative cyanobacterial diagnostics where appropriate. These findings demonstrate the utility of VNIRS, in most instances, as a valuable tool for reconstructing past cyanobacterial prevalence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".