Inter- and intra-observer variation in phytolith morphometry
Bibliographic record
Abstract
BACKGROUND: Archaeobotanists and palaeoecologists use geometric morphometrics extensively to identify plant opal phytoliths. Particularly when applied to assemblages of phytoliths from concentrations retrieved from closed contexts, morphometric data from archaeological phytoliths compared with similar data from reference material can allow taxonomic attribution. Observer variation is one aspect of phytolith morphometry that has received little attention but might be an important source of error and a potential cause of misidentification of plant remains. SCOPE: To investigate inter- and intra-observer variation in phytolith morphometry, eight researchers (observers) from different laboratories measured 50 samples each from three phytolith morphotypes (Bilobate, Bulliform flabellate and Elongate dendritic) three times, under the auspices of the International Committee for Phytolith Morphometrics (ICPM). METHODS: Data for 17 size and shape variables were collected for each phytolith by manually digitizing a phytolith outline (mask) from a photograph, followed by measurement of the mask with open-source morphometric software. KEY RESULTS: Inter-observer variation ranged from 0 to 23 % difference from the mean of all observers. Intra-observer variation ranged from 0 to 9 % difference from the mean of individual observers per week. Inter- and intra-observer variation was generally higher among inexperienced researchers. CONCLUSIONS: Scaling errors were a major cause of variation and occurred more with less experienced researchers, which is likely to be related to familiarity with data collection. The results indicate that inter- and intra-observer variation can be reduced substantially by providing clear instructions for and training with the equipment, photograph capturing, software, data collection and data cleaning. In this paper, the ICPM provides recommendations to minimize variation. Advances in automatic data collection might eventually reduce inter- and intra-observer variation, but until this is common practice, the ICPM recommends that phytolith morphometric analyses adhere to standardized guidelines to assure that measured phytolith variables are accurate, consistent and comparable between different researchers and laboratories.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".