Evaluating Two Observational Sampling Techniques for Determining the Distribution and Detection Probability of Age-0 Smallmouth Bass in Clear, Warmwater Streams
Bibliographic record
Abstract
Abstract Accurate determination of the distribution and abundance of age-0 sport fish, such as smallmouth bass Micropterus dolomieu, is an important aspect of successful species management (e.g., characterizing nursery habitats for protection or enhancement; identifying reproductive dynamics and influences of environmental variables). Two observational techniques—snorkeling and above-water (AW) observation—were evaluated in several Missouri Ozark streams to determine (1) accuracy of identifying age-0 smallmouth bass with each technique, (2) detection probabilities under a variety of channel unit (CU) conditions (i.e., discrete morphological features such as riffles or pools) and the relations between detection probability and hydraulic variables, and (3) behavioral responses (fright response, return response, or no response) of fish to the observers using each technique. Identification of age-0 smallmouth bass was over 90% accurate when using AW observation in shallow water and when snorkeling in deep water. In both streams, detection probabilities differed depending on the CU sampled and the method used. In deep pools, detection probabilities when snorkeling were two to three times those obtained from AW observation; however, AW observation was more efficient for sampling most shallow-water (<0.5-m) CUs. Velocity had no significant relation to detection probability, whereas depth was significantly related to detection probability for both sampling methods (the relationship was positive for snorkeling and negative for AW observation). The percentage of age-0 smallmouth bass that exhibited a fright response was greatest for fish observed by snorkeling in shallow-water CUs. On average, approximately 20% of fish showed a fright response, but a large proportion of these fish exhibited a return response. Our results indicate that multiple sampling methods may be necessary to achieve high accuracy when sampling small fish in a variety of CUs, and snorkeling and AW observation may be viable methods for selecting CUs in many warmwater streams. Additional work is recommended to address knowledge gaps, especially those related to the influence of biotic and abiotic variables that were not measured in this study. Received November 9, 2010; accepted May 12, 2011
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".