“Equipped by a set of fine and moving observations” : On the translation of Mary Hays's Memoirs of Emma Courtney into French
Bibliographic record
Abstract
This paper demonstrates the use of climatological data and published information to develop a thaw-freeze/freeze-kill indicator for fruit trees during flowering. In fruit-producing regions, when budding and flowering occur before the last spring freeze, a freeze-kill event can cause substantial losses. As spring onset is occurring earlier with climate change, thaw-freeze events have the potential to become more of a hazard both in terms of current production and in terms of potential adaptation strategies. To model the spring thaw-freeze and its magnitude or intensity, we proposed an indicator based on the accumulation of daily minimum temperature between successive freezing dates and its maximum value over the spring. This indicator was tested on apple and peach production in southern Ontario, Canada, using data from eight climate stations in southern Ontario. The indicator showed promise in its utility in that its magnitude was greater when freezing occurred after blooming and it was demonstrated to be correlated to the estimated blooming dates of apple and peach fruits grown in southern Ontario. The annual series was shown to fit the generalized extreme value distribution thereby allowing the extreme risk to be modelled and the return period to be calculated. It was also shown that the reported thaw-freeze events that caused significant apple and peach losses had a return period on the order of 10 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".