Development of an adaptive monitoring framework for long-term programs: An example using indicators of fish health
Bibliographic record
Abstract
Abstract Detecting unwanted changes associated with localized human activities in aquatic ecosystems requires defining the value of an indicator expected at a site in the absence of development. Ideally, adequate and comparable baseline data will be collected at an exposure location before that development, but this is rarely done. Instead, comparisons are made using various designs to overcome the inadequate or missing baseline data. Commonly these comparisons are done over short periods, using information from local reference sites to estimate variability expected at the exposed site. Results of these truncated designs are often evaluated using p values that may have little bearing on ecologically relevant changes. To remedy the reliance of studies on small datasets collected at reference sites, other designs emphasize regional analyses, but these may be insensitive to site-specific changes. Some designs also may forego discussing the consequences of detecting any differences. A new monitoring framework has been proposed to use existing solutions, simplify analysis, and focus on the detection of meaningful changes. It is illustrated here by using data on fish health from a large-scale, long-term program in the Moose River basin in northern Ontario. This framework advocates interpretation of data at multiple scales: within-site, locally, and regionally. The primary focus is on estimating a range from a probability distribution of historical data collected at a specific location where 95% of future observations are predicted to occur. Changes at the exposed site are also compared with historical and contemporary expectations from proximate and regional reference sites. Critical effect sizes also can be derived from regional reference data to evaluate the magnitude of differences observed between any 2 sites. Any unexpected changes inform future monitoring decisions provided by a priori guidance. Adoption of this framework extends the utility of monitoring programs in which commitments to long-term collections have been made, advocates harmonization of studies over time and space, and focuses attention on unusual observations. Integr Environ Assess Manag 2015;X:000–000. ©2015 SETAC. Key Points Understanding the relevance of changes is difficult and is an overlooked component of monitoring studies. Ecological relevance can be described with critical effect sizes and normal ranges. Critical effect sizes can be defined by sampling reference sites. Monitoring can be evaluated at multiple spatial and temporal scales to better understand the relevance of changes.
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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".