Primary data in pollination services mapping: potential service provision by honey bees (Apis mellifera) in Cumberland and Colchester, Nova Scotia
Bibliographic record
Abstract
This study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) pollination model to investigate the impacts of using field data on ecosystem services mapping studies by using potential pollination services in Cumberland and Colchester counties in Nova Scotia, Canada, as a case study. The model was run using two different sources of data to inform the floral resource component of the model. First, the model was run driven by proxies and approximate values gathered from the literature. The second iteration of the model utilized field-based data to generate floral resource abundance and distribution values. Both models utilized honey bee-specific species preference data drawn from field observations and expert opinion in the literature. Results indicated an 8.1% increase in the amount of land designated as high quality when the model was informed by field-based data. Such discrepancies are important when considering the potential implications of ecosystem services mapping studies on policy. As a tool, ecosystem services mapping provides the opportunity to spatially evaluate the impact that different land-use planning regimes could have on the supply and provision of various ecosystem services. However, as its popularity rises, it is important to keep in mind the accuracy and resolution of the data used to inform the modelling process.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.002 |
| Open science | 0.002 | 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".