Determining sampling date interval for precise in situ estimates of cumulative food consumption by fishes
Bibliographic record
Abstract
We tested the influence of sampling date interval (SDI) on precision of in situ estimates of cumulative food consumption by fishes. Daily rations of stream-dwelling green sunfish (Lepomis cyanellus) and impoundment-dwelling bluegill (Lepomis macrochirus) were estimated for 30 consecutive days using a low-effort procedure. Cumulative consumption by each species over the 30-day period (and 95% CIs) was estimated using Monte Carlo simulations. The effect of SDI on cumulative consumption estimates was examined by calculating cumulative consumption for SDIs of 1, 2, 3, 4, 5, 6, 7, 10, 14, and 30 days; the 1-day SDI served as a standard for evaluation of other SDIs. Cumulative consumption estimates began to fall outside the 95% CI for the 1-day SDI at SDIs of 3-4 days and did so with with increasing frequency as SDI increased. Error in estimating cumulative consumption was almost always [Formula: see text]15% relative to the 1-day SDI standard at SDIs of 5 days or less but was as high as 26 and 39% at SDIs of 6 and 7 days, respectively. Our results suggest that sampling at least every 5 days may be required to obtain precise estimates of cumulative consumption by fishes in lotic systems and small impoundments.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".