Discoveries and novel insights in ecology using structural equation modeling
Bibliographic record
Abstract
As we enter the era of data science (Lortie 2018), quantitative analysis methodologies are proliferating rapidly, leaving ecologists with the task of choosing among many alternatives. The use of structural equation modeling (SEM) by ecologists has increased in recent years, prompting us to ask users questions about their experience with the methodology. Responses indicate an enthusiastic endorsement of SEM. Two major elements of respondent’s experiences seem to contribute to their positive response, (1) a sense that they are obtaining more accurate explanatory understanding through the use of SEM and (2) excitement generated by the discovery of novel insights into their systems. We elaborate here on the detection of indirect effects, offsetting effects, and suppressed effects, and demonstrate how discovering these effects can advance ecology.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".