Citizen Science Data Show Temperature-Driven Declines in Riverine Sentinel Invertebrates
Bibliographic record
Abstract
We used the presence and absence data of sentinel invertebrates (stonefly, order Plecoptera) collected by citizen scientists over 17 years to approximate trends in stream health in urban Detroit, Michigan, USA. Citizen science data are commonly collected based on availability of limited funds. Thus, survey locations lack consistent data collection, and missing values are common. While citizen science data sets can be large, on a regional and local scale, they are often undervalued but present an opportunity for managers to inform their decisions if the missing data can be addressed. To overcome this hurdle, here, missing values were modeled with a combination of spatial (inverse distance weighting and spatial stream network), temporal (Bayesian state space), and machine learning (ensemble random forest) models combining atmospheric, hydrologic, and biologic data. Using the estimated missing values, we determined negative population trends in stoneflies driven by stream temperature via a dynamic occupancy model. Urban streams present a challenge to resource managers because data are collected at disparate locations and frequencies and inconsistently recorded. However, we show how a combination of methods with publicly available and citizen science data from across disciplines can inform managers and support land-use decisions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".