Agile Java(TM): Crafting Code with Test-Driven Development (Robert C. Martin Series)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biological invasions are a leading cause of global environmental change given their effects on both humans and biodiversity. Humans introduce invasive alien species and may facilitate their establishment and spread, which can alter ecosystem services, livelihoods, and human well-being. People perceive the benefits and costs of these species through the lens of diverse value systems; these perspectives influence decisions about when and where to manage them. Despite the entanglement of humans with invasive alien species, most research on the topic has focused on their ecological aspects. Only relatively recently have the human and social dimensions of invasions started to receive sustained attention in light of their importance for understanding and governing biological invasions. This editorial draws on contributions to a special issue on the "Human and Social Dimensions of Invasion Science" and other literature to elucidate major trends and current contributions in this research area. We examine the relation between humans and biological invasions in terms of four crosscutting themes: (1) how people cause biological invasions; (2) how people conceptualise and perceive them; (3) how people are affected - both positively and negatively - by them; and (4) how people respond to them. We also highlight several ways in which research on the human and social dimensions of invasion science improves understanding, stakeholder engagement, and management.
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.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it