Lakes as sensors in the landscape: Optical metrics as scalable sentinel responses to climate change
Bibliographic record
Abstract
As the lowest point in the surrounding landscape, lakes act as sensors in the landscape to provide insights into the response of both terrestrial and aquatic ecosystems to climate change. Here a novel suite of climate forcing optical indices (CFOI) from lakes across North America is found to respond to changes in air temperature, precipitation, and solar radiation at timescales ranging from a single storm event to seasonal changes to longer‐term interdecadal trends with regression r2 values ranging from 0.73 to 0.89. These indices are based on two optical metrics of dissolved organic carbon (DOC) quality: DOC specific absorbance (a*320) and spectral slope (S275–295), where the ratio a*320 to S275–295 gives a composite climate forcing index. These indices of DOC quality are more responsive to climate forcing than is DOC concentration. A similar relationship between the component indices a*320 and S275–295 is observed across a wide range of lake types. A conceptual model is used to examine the similarities and differences in DOC‐related mechanisms and ecological consequences due to increased temperature vs. precipitation. While both warmer and wetter conditions increase thermal stratification, these two types of climate forcing will have opposite effects on water transparency as well as many ecological consequences, including oxygen depletion, the balance between autotrophy and heterotrophy, and depth distributions of phytoplankton and zooplankton.
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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 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".