Plant collecting spread and densities: their potential impact on biogeographical studies in Thailand
Bibliographic record
Abstract
Abstract Aims To produce representative aggregate maps of plant collection locations in Thailand and discuss their impact on biogeographical studies in Thailand and the surrounding region. Location Thailand. Methods A representative data set comprising 6593 plant specimen records for Thailand has been assembled. The data set contains ± all known collections for fifteen representative plant families and further records for another 104. All records are localized to Changwat (province), 6441 to at least quarter degree square. Results Analysis shows that the spread of collecting activity in Thailand is markedly uneven; 20% of collections come from a single Changwat (Chiang Mai) and 53% of Changwat have fifty or fewer collections. The distribution of collections by Changwat and by quarter degree square is erratic with most squares and Changwat having few collections, both in proportionate and absolute terms. Some of the most densely forested Changwats and squares appear undercollected. Distribution maps for common, easily recognized tree species in the genus Syzygium show distributional gaps. Conclusions Thailand is defined as an undercollected country. Even within the few well‐collected quarter degree squares the spread of collecting is still poor; almost all collections being localized to one of three mountain ranges or their foothills. There are many gaps in collecting activity which make impossible a straightforward interpretation of biogeographical pattern. It is argued that targeted collecting activity is needed, that assembly of this type of data set is therefore essential and that our data set and its interpretation is a model for all countries in the region.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".