Is the sampling strategy interfering with the study of spatial variability of zooplankton communities?
Bibliographic record
Abstract
Surveys at the whole-lake scale take some time to carry out: several hours or several days. For logistic reasons, the sites are not sampled simultaneously or in a random sequence. Traditional limnological sampling methods require an appreciable amount of time at each site. Any sampling strategy that is not random or simultaneous introduces dependencies among the observations, which must be taken into account during the analysis and interpretation of the data. What is the real nature of the variation measured using a given sampling design? This question is approached using sites sampled by two boat teams during two consecutive days. Statistical modelling was used to partition the variation of zooplankton size-class data into environmental and spatial components. The conclusions reached after an analysis that did not control for the sampling design are erroneous and quite different from those reached when the effect of the sampling design (factors Day, Boat, and Hour) was taken into account. Clearly, when a significant effect of the sampling design is found, one must control for it during the analysis and interpretation of ecological variation.
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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 | 0.210 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".