SDesti: An R package for the analysis of aquatic benthos environmental studies’ data
Bibliographic record
Abstract
Data analysis is one of the most relevant steps of aquatic benthic environmental monitoring and research studies, and should be a fundamental consideration in both the planning (i.e., defining appropriate sampling design strategies) and implementation phases (application of appropriate standardized sampling procedures). A common objective of these studies is to identify relationships between environmental stressors and benthic bioindicator metrics. However, assessing these relationships is a complex process. Multivariate regression model adjustment coupled with forward and backward model selection routines is an appropriate complementary statistical analysis tool to test for the existence of statistically significant associations between a non-autocorrelated biological response and each variable within a group of environmental covariates included in a model. With this in mind, we developed SDesti, a user-friendly R package to analyze benthos data (number of individuals, biomass, chlorophyll concentration, or biological indices, excluding beta diversity metrics). SDesti contains four user accessible functions. AnalysisDescriptives() and Estimation() give information on the quality, homogeneity and representativeness of the data for one sampling campaign for one site. TimeLineAnalysisDescriptives() performs the descriptive analysis that usually precedes the adjustment of a regression model. TimeLineAnalysis() automatically adjusts an adequate regression model (linear, Poisson, quasipoisson, or negative binomial) and also returns the necessary measures and graphics to evaluate the quality of the adjustment and verify the model assumptions. SDesti greatly simplifies the process of data analysis and can be easily used by non-statisticians. The analytical package includes a complete manual that provides detailed information: on the data structure requirements, on the variable nomenclature rules and program operating procedures, on the data analysis (complemented with examples) and on the interpretation of the results (type ??SDesti on R console). SDesti eliminates redundancy, reduces human error and, coupled with a suitable sampling design, standard sampling and sample treatment procedures, it contributes to improve the consistency of the results in environmental studies. SDesti binary for windows users and installation instructions can be found below. Compiled for R 4.3.2 version. Refer to the program PDF manual for a detailed description of the data structures, functions, data analyses and interpretation of results. Type ??SDesti on R, or RStudio consoles and select the PDF file. Note: RStudio 2023.09.1 has a bug that delivers an error message when trying to open PDF vignettes (program manuals). Use R 4.3.2 console to open SDesti's PDF manual.
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 | 0.001 | 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 | 0.003 | 0.003 |
| 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".