Assessment of the Use and Benefits of the Invasive Species Compendium
Bibliographic record
Abstract
The use of information and communication technology, including e-resources, to deliver information has expanded rapidly in recent years.They have certain advantages including quick access to relevant and current information that may not be available through other means.There are several online resources that provide information about invasive species including the Invasive Species Compendium (ISC).There has been limited work to assess these websites, including whether they provide up-to-date information, the extent of their global coverage, and how users perceive their usefulness.This study assesses such factors for the ISC, analysing usage statistics, data from three user surveys and information from key informant interviews.Findings show that the ISC is highly valued by users, in particular researchers, and that the information is up to date, reliable, and open access, which is of particular value to those working in the Global South.While considerable use of the ISC is to seek out information related to invasive species, it is apparent that other users explore the website to discover information to help manage crop pests and diseases.Key feedback included how users access datasheets within the ISC, and the low level of awareness of the resource.Going forward, it is critical that the ISC can remain as an open access resource, with sufficient funding to ensure it is continuously updated.This will enable researchers in the Global South to continue to access key data and literature, while concentrating resources on fieldwork, pest risk analysis, management and control.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".