Having it all: hybridizing conventional and community science monitoring for enhanced data quality and cost savings
Bibliographic record
Abstract
Large-scale monitoring is used to track population trends for many ecologically and economically important wildlife species. Often, population monitoring involves professional staff travelling to collect data (i.e., conventional monitoring) or in efforts to reduce monitoring costs, by engaging volunteers (i.e., community science). Although many studies have discussed the advantages and disadvantages of conventional vs. community science monitoring, few have made direct, quantitative comparisons between these two approaches. We compared data quality and financial costs between contemporaneous and overlapping conventional and community science programs for monitoring a major forest pest, the spruce budworm ( Choristoneura fumiferanae Clem.). Although community science trapping sites were clumped around urban areas, abundance estimates from the programs were strongly spatially correlated. However, annual program expenditures were nearly four times lower in the community science versus the conventional program. We modelled a hypothetical hybrid model of the two programs, which provided full spatial coverage and potentially the same data, but at half the cost of the conventional program and with the added opportunity for public engagement. Our study provides a unique quantitative analysis of merits and costs of conventional versus community science monitoring. Our study offers insights on how to assess wildlife monitoring programs where multiple approaches exist.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".