Methods manual IV : sediment toxicity testing, field and laboratory methods and data management
Bibliographic record
Abstract
Over the past ten years the National Water Research Institute has conducted major programmes in both the Great Lakes and Fraser River as part of a programme to establish a national reference database on benthic invertebrates for Canada. A critical part of this programme is the establishment of a standard set of protocols and methods for all phases of data collection and processing. This document attempts to provide that written record of the methods being used at the institute. There are three components to the database; Data sets describing the invertebrate fauna, data sets describing the toxicity of sediments based on four invertebrate tests and data sets describing the environmental attributes of sites. In this protocols document we have described the methods being used in a sequential manner. The first section deals with field procedures, collection of both biological and environmental data, in both lentic and lotic habitats. The second section addresses the laboratory procedures regarding sample handling and sample processing for biological samples. The third addresses the issue of data management. In addition we have provided a set of tables and forms used by the programme. We hope that this will provide a useful resource for others involved in this type of work.
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 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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.218 | 0.229 |
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".