Recent temporal trend monitoring of mercury in Arctic biota ? how powerful are the existing data sets?
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".