Predictive modeling of marine benthic macrofauna and its use to inform spatial monitoring design
Bibliographic record
Abstract
This study undertakes ecological analysis focused on predictive modelling and design for spatial sampling. The approaches are applied to a set of coastal marine benthic macrofaunal observations, and associated environmental data, measured at 48 sites in St Anns Bay, Nova Scotia, Canada. A multivariate generalized least-squares regression was used to establish a predictive relationship between benthic fauna and the environment. Five ecological indices derived from faunal composition (abundance, richness, species number, diversity, AMBI) were treated as a multivariate response, and 10 environmental variables as candidate predictors. The multivariate regression also incorporated the effects of spatial autocorrelation. Predictive relationships were highly significant, and variable selection identified three key environmental predictors (median sediment grain size, porosity, and sulfide). Using these baseline data, we developed a procedure to identify a reduced sampling design for long-term monitoring of benthic faunal health. The procedure is based on a sequential (backward elimination) algorithm to identify the set of sites that contributed most to the overall information. This study provides a general and comprehensive statistical framework for treating environmental monitoring and sampling design. It can be extended beyond the statistical framework used, and applied to a range of ecological applications.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".