Navigating methodological decisions: Balancing rigor and data volume of the Canadian Living Planet Index
Bibliographic record
Abstract
Abstract The Living Planet Index — a biodiversity indicator that assesses the relative change of aggregate vertebrate abundance data — is an indicator used in global and national biodiversity monitoring frameworks. In Canada, the LPI has been modified (C-LPI) — adopting differing methodological choices relative to the global LPI. However, there is no clear consensus on the most appropriate analytical methods, particularly as they pertain to the treatment of zeros, confidence intervals and uncertainty, time series length and number of data points required, modelling of short time series, removal of outliers, weighting species, and the impact of baseline year selection. Our analysis transparently explores multiple methodological options and the consequent C-LPI output for each of these decision points. Our research does not evaluate the superiority of a single approach but rather provides transparency and accountability in C-LPI reporting. We hope that this will further strengthen the utility of the C-LPI and provide decision makers with the necessary information to appropriately interpret patterns, evaluate progress towards biodiversity targets, and inform conservation action.
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 | 0.684 | 0.854 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.023 | 0.006 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier 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".