Semantic Assessments of Experienced Biodiversity from Photographs and On-Site Observations – A Comparison
Bibliographic record
Abstract
Since the 1960’s, public assessments of landscapes have often been carried out using photographic representations. How reliable and valid are these assessments compared with on-site observations? In the present study, participants have been asked to judge different areas in terms of a limited feature: the biodiversity of the area. Digitalized photos from six different study areas were made available on the Internet, along with a questionnaire consisting of a semantic form with specific words/expressions to be rated in relation to the photos (four per area). Participants were recruited via mailing lists and informal contacts. These results were compared with a study in which students and ecologists had rated the same places using the same form, but this time on-site. The Internet participants were also asked to state their profession/education to make comparisons possible. The comparisons revealed differences between on-site and photo-based ratings, but the main difference was expressed by on-site biologists regarding areas with the highest experienced biodiversity values, possibly due to their higher degree of expertise and use of more senses than can be used when judging photographs. Concerning laymen in particular, it is concluded that the comparison between on-site and photo-based ratings is not conclusive enough to allow us to determine whether it is appropriate to use one method as a substitute for the other.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".