IMPACT ASSESSMENT MODEL FOR CLEAR WATER FISHES EXPOSED TO EXCESSIVELY CLOUDY WATER<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT: A new type of empirical model described here enables real time assessment of impacts caused by excessive water cloudiness as a function of (a) reduced visual clarity (excessive cloudiness) and (b) duration of exposure to cloudy conditions, in fisheries or fish life stages adapted to life in clear water ecosystems. This model takes the familiar form used in earlier suspended sediment dose effect models image where z is severity of ill effect (SEV), x is duration of exposure (h), y is black disk sighting range ( y BD, m)—a measure of water clarity, a is the intercept, and b and c are slope coefficients. As calibrated in this study the model is image Severity of ill effect is ranked on a 15‐step scale that ranges from 0 to 14, where zero represents nil effect and 14 represents 100 percent mortality. This model, based on peer consultation and limited meta analysis of peer reviewed reports, accomplishes the following: (a) identifies the threshold of the onset of ill effects among clear water fishes; (b) postulates the rate at which serious ill effects are likely to escalate as a function of reduced visual clarity and persistence; (c) provides a context (the “visual clarity” matrix, with its cell coordinates) to share and compare information about impacts as a function of visual clarity “climate” (d) demonstrates changes in predator prey interactions at exposures greater than and less than the threshold of direct ill effects; (e) calibrates trout reactive distance (cm) as function of water clarity in the form image where y represents reactive distance (cm) and x represents visual clarity (black disk sighting range, cm), and where a and b are intercept and slope respectively, such that image (f) identifies black disk sighting range, in meters, and its reciprocal, beam attenuation, as preferred monitoring variables; and (g) provides two additional optical quality variables (Secchi disk extinction distance and turbidity) which, suitably calibrated as they have been in this study, expand the range of monitoring options in situations in which the preferred technology—beam attenuation equipment or black disk sighting equipment—is unavailable or impractical to use. This new model demonstrates the efficacy of peer collaboration and defines new research horizons for its refinement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".