Bibliographic record
Abstract
The decision making process used by the NZ government with respect to conservation issues today, requires specific, descriptive output regarding research summaries. Implementing the use of a global, linear regression may inform stakeholders about the general importance of parameters of interest for a study area or a species of interest. However, the broad brush stroke of the global model may well obscure what is happening at a localised and spatially important scale. Temporal data are difficult to visualise. The field of geovisual analytics is producing new methodologies to incorporate time series, specifically utilising spatially significant output from Geographically Weighted Regression (GWR) to explore the complexities of spatial-temporal relationships such as 14 years of cod-invertebrate symbiosis on the Newfoundland Shelf (Windle et al. 2012), coastal water quality patterns in south Australia (Bierman et al. 2011), and from information visualisation research, exploring the combination of spatial statistics with geovisual analytics (Andrienko et al. 2007; Demsar et al. 2008a; Demsar et al. 2008b; Andrienko et al. 2010a; Andrienko et al. 2010b; Foley & Demsar 2013; Andrienko & Andrienko 2013).
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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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; both teacher heads agree on what is shown here.
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".