A method for estimating the diffuse attenuation coefficient (<i>K</i><sub><i>d</i>PAR</sub>) from paired temperature sensors
Bibliographic record
Abstract
Abstract A new method for estimating the diffuse attenuation coefficient for photosynthetically active radiation (KdPAR) from paired temperature sensors was derived. We show that during cases where the attenuation of penetrating shortwave solar radiation is the dominant source of temperature changes, time series measurements of water temperatures at multiple depths (z1 and z2) are related to one another by a linear scaling factor (α). KdPAR can then be estimated by the simple equation KdPAR = ln(α)/(z2−z1). A suggested workflow is presented that outlines procedures for calculating KdPAR according to this paired temperature sensor (PTS) method. This method is best suited for conditions when radiative temperature gains are large relative to physical noise. These conditions occur frequently on water bodies with low wind and/or high KdPARs but can be used for other types of lakes during time periods of low wind and/or where spatially redundant measurements of temperatures are available. The optimal vertical placement of temperature sensors according to a priori knowledge of KdPAR is also described. This information can be used to inform the design of future sensor deployments using the PTS method or for campaigns where characterizing sub‐daily changes in temperatures is important. The PTS method provides a novel method to characterize light attenuation in aquatic ecosystems without expensive radiometric equipment or the user subjectivity inherent in Secchi depth measurements. This method also can enable the estimation of KdPAR at higher frequencies than many manual monitoring programs allow.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".