Recent temporal trend monitoring of mercury in Arctic biota ? how powerful are the existing data sets?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The goal of this paper is to describe and discuss statistical power with respect to mercury in Arctic biota, using data gathered during the past two or three decades, mostly under the auspices of AMAP Phases I and II. It will describe the current levels of power of existing data sets to detect temporal trends of Hg concentrations. If the desired power is fixed to an appropriate magnitude, the minimum size of a detectable trend within a specified time period or the number of years that is required to detect a certain trend could be estimated provided that the random between-year variation for the current time-series is known. These various measures of performance of the AMAP mercury time-series, derived from the power analysis, are discussed in some detail. The number of years required to detect a certain trend at a particular power at a specific Type I error rate (alpha) is compared with the actual number of years available when the AMAP Phase II assessment was carried out. In general the investigated time-series were too short to possess an acceptable statistical power. The effect of varying the Type-I error rate, the slope of a trend and the desired power is investigated to rank the importance of the various components regulating the statistical power. The consequence of sampling less frequently than once a year is considerable loss of power.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it